Method for selecting medical and biochemical diagnostic tests using neural network-related applications

• US 6,678,669 B2
• Filed: 08/14/1997
• Issued: 01/13/2004
• Est. Priority Date: 02/09/1996
• Status: Expired due to Fees

• Current Assignee: Cytyc Corporation (Hologic Incorporated)
• Original Assignee: Cytyc Prenatal Products Corp. (Hologic Incorporated)
• Inventors: Lapointe, Jerome, DeSieno, Duane
• Primary Examiner(s): Powell, Mark R.
• Assistant Examiner(s): STARKS, WILBERT L
• Application Number:
  US08/912,133
• Publication Number:
  US 20010023419A1
• Time in Patent Office: 2,343 Days
• Field of Search: 706/15, 702/82, 702/121, 702/123
• US Class Current: 706/15
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Methods are provided for developing medical diagnostic tests using decision-support systems, such as neural networks. Patient data or information, typically patient history or clinical data, are analyzed by the decision-support systems to identify important or relevant variables and decision-support systems are trained on the patient data. Patient data are augmented by biochemical test data, or results, where available, to refine performance. The resulting decision-support systems are employed to evaluate specific observation values and test results, to guide the development of biochemical or other diagnostic tests, too assess a course of treatment, to identify new diagnostic tests and disease markers, to identify useful therapies, and to provide the decision-support functionality for the test. Methods for identification of important input variables for a medical diagnostic tests for use in training the decision-support systems to guide the development of the tests, for improving the sensitivity and specificity of such tests, and for selecting diagnostic tests that improve overall diagnosis of, or potential for, a disease state and that permit the effectiveness of a selected therapeutic protocol to be assessed are provided. The methods for identification can be applied in any field in which statistics are used to determine outcomes. A method for evaluating the effectiveness of any given diagnostic test is also provided.

Claims

1. A computer-based method for selecting variables for use in a computer-based system for prognosis, diagnosis and for predicting outcomes based upon a plurality of variables, comprising:

(a) obtaining a set of candidate variables based upon data obtained by observing or detecting an event or making physical or chemical observations or measurements or obtaining data resulting from tests, designating the candidate variables as a first set of candidate variables and entering them into a computer memory or computer-readable storage medium;

(b) providing a set of selected important variables and designating it the current set of selected important variables, wherein the set of selected important variables is initially empty;

(c) taking candidate variables from the set of candidate variables one at a time and evaluating each by training a computer-based decision-support system on that variable combined with the current set of selected important variables;

(d) selecting the best of the candidate variables, wherein the best variable is any one that gives the highest performance of the decision-support system, and if the best candidate variable improves performance compared to the performance of the selected important variables, adding it to the selected important variable set, removing it from the candidate set and continuing evaluating at step (c), wherein, when the best candidate variable does not improve performance, the process is completed;

(e) producing an output that comprises the resulting set of selected variables.

2. The method of claim 1, wherein in step (a) the candidate variables are obtained from patients and include historical data and/or biochemical data.

3. A method of diagnosis, comprising:

selecting a set of important selected variables according to the method of claim 1; and

training a computer-based decision-support system using the selected final set of important selected variables to produce a test for diagnosis;

examining or querying a patient; and

entering the resulting patient data into the trained decision-support system; and

producing an output comprising a diagnostic indicator.

4. The method of claim 3, wherein the method of diagnosis assesses the likelihood that a medical condition or disorder is present, assesses the likelihood that a particular condition will develop or occur in the future, selects a course of treatment or determines the effectiveness of a treatment.

5. The method of claim 4, wherein the condition is a pregnancy-related condition or endometriosis.

6. The method of claim 5, wherein the pregnancy-related condition is preterm delivery or risk of delivery within a selected time period.

7. The method of claim 3, wherein the method of diagnosis assesses the presence, absence or severity of a medical condition or determines the likely outcome resulting of a course of treatment.

8. The method of claim 3, further comprising training a final decision-support system based on the completed set of selected important variables to produce a decision-support system based test for the condition.

9. The method of claim 3, wherein the condition is a gynecological condition.

10. The method of claim 9, wherein the condition is selected from among infertility, a pregnancy related event, and pre-eclampsia.

11. The method of claim 3, wherein the candidate variables include biochemical test data.

12. The method of claim 3, wherein the trained computer-based decision-support system includes a system selected from the group consisting of expert system, fuzzy logic systems, non-linear regression analysis system, multivariate analysis system, decision tree classifiers, Bayesian belief networks and neural networks.

13. The method of claim 3, wherein the trained computer-based decision-support system comprises a neural network or plurality of neural networks.

14. The method of claim 3, further comprising obtaining patient-data and entering such data into the trained decision-support system;

and

providing a diagnosis to the patient.

15. A computer-based diagnostic test produced by the method of claim 3.

16. A method of improving the effectiveness of a disnostic biochemical test, comprising:

selecting a set of important selected variable according to the method of claim 1 to identify a set of selected variable by collecting candidate variables from test subjects;

performing the biochemical test on test subjects to obtain test data, wherein the biochemical test is performed before, after or during the selecting step; and

training a decision-support system using the selected final set of important selected varibales and the biochemical test data; and

producing an output that is a test, wherein the output test is more effective than the biochemical test alone.

17. The method of claim 16, wherein the candidate variables are selected from the group consisting of:

Age;

Ethnic origin Caucasian;

Ethnic origin Black;

ethnic origin Asian;

ethnic origin Hispanic;

ethnic origin Native American;

ethnic origin other than the Native American, Hispanic, Asian, Black, or Caucasian;

martial status single;

martial status married;

martial status divorced or separated;

martial status widowed;

martial status living with partner;

martial status other than married, divorced/separated, widowed, or living with partner;

education unknown;

education less than high school;

education high school graduate;

education college or trade school;

patient has Uterine Contraction with or without pain;

patient has intermittent lower abdominal pain, dull, low backache pelvic pressure;

patient has bleeding during the second or third trimester;

patient has menstrual-like or intestinal cramping;

patient has change in vaginal discharge or amount, color, or consistency;

patient is not “

feeling right”

;

pooling;

ferning;

nitrazine;

estimated gestational age (EGA) based on last menstrual period (LMP);

EGA by sonogram (SONO);

EGA by best, wherein EGA by best refers to the best of EGA by SONO and EGA by LMP determined as follows;

if EGA by SONO is <

13 weeks, then EGA best is EGA SONO;

if the difference by EGA by LMP and EGA by SONO is >

2 weeks, then EGA best is EGA by SONO;

otherwise EGA best is EGA by LMP;

EGA at sampling;

cervical dilatation (CD);

gravity;

parity-term;

parity-preterm;

parity-abortions, wherein the number of abortions include spontaneous and elective abortions;

parity-living;

sex within 24 prior to sampling for fFN;

vaginal bleeding at time of sampling;

cervical consistency at time of sampling;

uterine contractions per hour as interpreted by the physician;

no previous pregnancies;

at least one previous pregnancy without complications;

at least one preterm delivery;

at least one previous pregnancy with a premature rupture of membrane (PROM);

at least one previous delivery with incompetent cervix;

at least on previous pregnancy with pregnancy induced hypertension (PIH)/preeclampsia;

at least one previous pregnancy with spontaneous abortion prior to 20 weeks; and

at least one previous pregnancy with a complication not listed above.

18. The method of claim 17, wherein the biochemical test is a test that detects fetal fibronectin in cervico/vaginal samples.

19. A method of identifying a biochemical test that aids in diagnosis of a disorder or condition, comprising:

(a) selecting a set of important selected variables according to the method of claim 1 by collecting candidate variables from test subjects by performing one or a plurality of biochemical tests to obtain biochemical test data from test subjects, wherein the biochemical test(s) is performed before, after or during the selecting step;

(b) identifying a set of biochemical test data, and training a computer-based decision-support system using the selected final set of important selected variables combined with each member of the set of biochemical test data, and assessing the performance of the resulting system;

(c) repeating the training and assessing with each member of the set of biochemical test data until all have been used in a training;

(d) selecting the members of the set of biochemical data that results in a decision-support system that performs the best; and

(e) producing a biochemical test that measures the selected biochemical data.

20. The method of claim 1 that is a computer-assisted method, wherein the set of n candidate variables and set of selected important variables are each stored in a computer.

21. The method of claim 1, wherein the computer-based decision-support system includes a system selected from the group consisting of expert systems, fuzzy logic systems, non-linear regression analysis systems, multivariate analysis systems, decision tree classifiers, Bayesian belief networks and neural networks.

22. The method of claim 1, wherein the computer-based decision-support system comprises a neural network or plurality of neural networks.

23. The method of claim 1 that is for generating variables for use in systems for diagnosing medical conditions or prognosticating medical conditions.

24. A computer system programmed with instructions for performing the method of claim 1.

25. A computer-readable medium, comprising instructions for performing the method of claim 1.

26. A computer-readable medium, comprising the set variables produced as the output of claim 1.

27. A method for variable selection, comprising:

(a) collecting a first set of n candidate variable that correspond to measurements, the results of tests, queries or observations and a second set of selected important variables, wherein the second set is initially empty;

(b) ranking all candidate variables, wherein the ranking is either arbitrary or ordered;

(c) taking the highest m ranked variable one at a time, wherein m is from 1 up to n, and evaluating each by training a decision-support system on that variable combined with the current set of selected important variables;

(d) selecting the best of the m variables, wherein the best variable is the one that gives the highest performance of the decision-support system, and if the best variable improves performance compared to the performance of the selected important variables, adding it to the selected important variable set, removing it from the candidate set and continuing evaluating at step (c), if the variable does not improve performance in comparison to the performance of the selected important variables, evaluating is continued at step (e);

(e) determining if all variable on the candidate set have been evaluated, wherein if they have been evaluated, the process is complete and the set of selected important variables is a completed set, otherwise continuing by taking the next highest m ranked variables one at a time, and evaluating each by training a decision-support-system on that variable combined with the current set of important selected variables and performing step (d); and

(f) producing an output comprising the set of important variables.

28. The method of claim 27, wherein the candidate variables include biochemical test data.

29. The method of claim 27, wherein ranking is based on an analysis comprising a sensitivity analysis or other decision-support system-based anaylsis.

30. The method of claim 27, wherein ranking is based on a process comprising a statistical analysis.

31. The method of claim 27, wherein ranking is based on a process comprising chi square, regression analysis or discriminant analysis.

32. The method of claim 27, wherein ranking is determined by a process that uses evaluation by an expert, a rule based system, a sensitivity analysis or combinations thereof.

33. The method of claim 27, wherein the sensitivity analysis, comprises:

(i) determining an average observation value for each of the variables in the observation data set;

(ii) selecting a training example, and running the example through a decision-support system to produce an output value, designated and stored as the normal output;

(iii) selecting a first variable in the selected training example, replacing the observation value with the average observation value of the first variable, running the modified example in the decision-support system in the forward mode and recording the output as the modified output;

(iv) squaring the difference between the normal output and the modified output and accumulating it as a total, wherein the total for each variable designated the selected variable total for each variable;

(v) repeating steps (iii) and (iv) for each variable in the example;

(vi) repeating steps (ii)-(v) for each example in the data set, wherein each total for the selected variable represents the relative contribution of each variable to the determination of the decision-support system output.

34. The method of claim 27, wherein the decision-support system includes a consensus of neural networks.

35. The method of claim 27, that is a computer-assisted method, wherein the set of n candidate variables and set of selected important variables are each stored in a computer.

36. The method of claim 27, further comprising training a final decision-support system based on the completed set of selected important variables to produce a decision-support system based test for the condition.

37. A method for variable selection, comprising:

(a) providing a first set of n candidate variable and a second set of selected important variables, wherein the second set is initially empty;

(b) taking candidate variable one at a time evaluating each by training a computer-based decision-support system on that variable combined with the current set of selected important variables;

(c) selecting the best of the candidate variable, wherein the best variable is any one that gives the highest performance of the decision-support system, and if the best candidate variable improves performance compared to the performance of the selected important variables, adding it to the selected important variable set, removing it from the candidate set and continuing evaluating at step (b), wherein the best candidate variable does not improve performance, the process is completed, wherein the computer-based decision-support support system includes a consensus of neural networks; and

(d) outputting the candidate variables onto a computer display or storing them on computer readable medium.

38. A method for developing a decision-support system-based test to aid in diagnosing a medical condition, disease or disorder in a patient, comprising:

(a) collecting observation by examining and querying a group of test patients in whom the medical condition is known;

(b) categorizing the observations into a set of candidate variable having observation values and storing the observation values as a observation data set in a computer;

(c) selecting a subset of selected important variables from the set of candidate variables by classifying the observation data set using a first decision-support system programmed into the computer system, whereby the subset of selected important variables includes the candidate variables substantially indicative of the medical condition; and

(d) training a computer-based second decision-support system using the observation data corresponding to a subset of selected important variables, whereby the second decision-support system-based system constitutes a decision-support based diagnostic test for the condition, disease or disorder; and

(e) storing the second decision-support system in a computer memory or on a computer-readable medium.

39. The method of claim 38, wherein the first decision-support system includes at least one neural network.

40. The method of claim 38, wherein the second decision-support system includes least one neural network.

41. The method of claim 38, wherein the step of selecting a subset of selected important variables includes:

(i) providing a first set of n candidate variables and a second set of selected important variable, wherein the second set is initially empty;

(ii) taking candidate variables one at a time and evaluating each by training a decision-support system on that variable combined with the current set of selected important variables;

(iii) selecting the best of the candidate variables, wherein the best variable is any one that gives the highest performance of the decision-support system, and if the best candidate variable improves performance compared to the performance of the selected important variables, adding it to the selected important variable set, removing it form the candidate set and continuing evaluating at step (ii), wherein the best candidate variable does not improve performance, the process is completed.

42. The method of claim 38, wherein the step of selecting a subset of selected important variable includes:

(i) providing a first set of n candidate variables and a second set of selected important varibales, wherein the second set is initially empty;

(ii) ranking all candidate variables, wherein the ranking is either arbitrary or ordered;

(iii) taking the highest m ranked variables one at a time, wherein m is from 1 up to n, and evaluating each by training a decision-support system on that variable combined with the current set of selected important variables;

(iv) selecting the best of the m variables, wherein the best variable is the one that gives the highest performance of the decision-support system, and if the best variable improves performance compared to the performance of the selected important variable, adding it to the selected important variable set, removing it from the candidate set and continuing evaluating at step (iii), if the variable does not improve performance in comparison to the performance of the selected important variables, evaluating is continued at step (v);

(v) determining if all variables on the candidate set have been evaluated, wherein if they have been evaluated, the process is complete and the set of selected important variables is a completed set, otherwise continuing by taking the next highest m ranked variables one at a time, and evaluating each by training a decision-support-system on that variable combined with the current set of important selected variables and performing step (iv).

43. The method of claim 42, wherein the candidate variables include biochemical test data.

44. The method of claim 42, wherein ranking is based on an analysis comprising a sensitivity analysis or other decision-support system-based analysis.

45. The method of claim 44, wherein the sensitivity analysis, comprises:

(i) determining an average observation value for each of the variables in the observation data set;

(ii) selecting a training example, and running the example through a decision-support system to produce and output value, designated and stored as the normal output;

(iii) selecting a first variable in the selected training example, replacing the observation value with the average observation value of the first variable, running the modified example in the decision-support system in the forward mode and recording the output as the modified output;

(iv) squaring the difference between the normal output and the modified output and accumulating it as a total, wherein the total for each variable designated the selected variable total for each variable;

(v) repeating steps (iii) and (iv) for each variable in the example;

(vi) repeating steps (ii)-(v) for each example in the data set, wherein each total for the selected variable represents the relative contribution of each variable to the determination of the decision-support system output.

46. The method of claim 42, wherein ranking is based on a process comprising a statistical analysis.

47. The method of claim 42, wherein ranking is based on a process comprising chi square, regression analysis or discriminant analysis.

48. The method of claim 42, wherein ranking is determined by a process that use evaluation by an expert, a rule based system, a sensitivity analysis or combinations thereof.

49. The method of claim 48, wherein the sensitivity analysis comprises:

(i) determining an average observation value for each of the variables in the observation data set;

(ii) selecting a training example, and running the example through a decision-support system to produce an output value, designated and stored as the normal output;

(iii) selecting a first variable in the selected training example, replacing the observation value with the average observation value of the first variable, running the modified example in the decision-support system in the forward mode and recording the output as the modified output;

(iv) squaring the difference between the normal output and the modified output and accumulating it as a total, wherein the total for each variable designated the selected variable total for each variable;

(v) repeating steps (iii) and (iv) for each variable in the example;

(vi) repeating steps (ii)-(v) for each example in the data set, wherein each total for the selected variable represents the relative contribution of each variable to the determination of the decision-support system output.

50. The method of claim 49, further comprising:

(vii) ranking the variables according to their relative contribution to the determination of the decision-support system output.

51. The method of claim 38, wherein the step of training a second decision-support system includes a validating step wherein a previously-unused set of observation data is run through the second decision-support system after training to provide a performance estimate for indication of the medical condition, wherein the previously-unused set of observation data are collected from patients in whom the medical condition is known.

52. The method of claim 38, wherein the step of training a second decision-support system includes partitioning the observation data set into a plurality of partitions comprising at least one testing data partition and a plurality of training data partitions, wherein the second decision-support system is run using the plurality of training data partitions and the testing data partition is used to provide a final performance estimate for the second decision-support system after the training data partitions have been run.

53. The method of claim 52, wherein the second decision-support system comprises a plurality of neural networks, each neural network of the plurality having a unique set of starting weights and having a performance rating value.

54. The method of claim 53, wherein the final performance estimate is generated by averaging the performance rating values for the plurality of neural networks.

55. The method of claim 38, wherein the observation values are obtained from patient historical data results and/or biochemical test results.

56. The method of claim 55, wherein the pregnancy related condition is preterm delivery or risk of delivery within a selected time period.

57. The method of claim 38, wherein the condition is a pregnancy-related condition or endometriosis.

58. The method of claim 38, further comprising:

(e) collecting additional observations from patients and categorizing them into a set of candidate variables, which are then added to first set of candidate variables; and

then

(f) repeating steps (c) and (d).

59. The method of claim 38, wherein the test assesses the presence, absence, severity or course of treatment of a disease, disorder or other medical condition or aids in determining the outcome resulting from a selected treatment.

60. The method of claim 38, further comprising, before, during or after collecting observations from a group of test patients and before training the second decision-support based system,

performing biochemical tests on at least one test patient and collecting test results of a biochemical test from at least one or a portion of the test patients in whom the condition is known or suspected and categorizing them into a set of candidate variables, which are then added to first set of candidate variables;

and then

repeating steps (c) and (d).

61. The method of claim 60, wherein the test assesses the presence, absence, severity or course of treatment of a disease, disorder or other medical condition or aids in determining the outcome resulting from a selected treatment.

62. The method of claim 60, wherein the decision-support system includes a neural network and the final set constitutes a consensus of neural networks.

63. The method of claim 60, further comprising identifying any biochemical test data variable(s) that end up in the final subset of selected important variables, whereby the identified biochemical test data variable(s) serve as indicators of the disease, disorder or condition.

64. The method of claim 63, wherein the test assesses the presence, absence, severity or course of treatment of a disease, disorder or other medical condition or aids in determining the outcome resulting from a selected treatment.

65. The method of claim 63, wherein the decision-support system includes a neural network and the final set constitutes a consensus of neural networks.

66. The method of claim 63, wherein the first subset of relevant variables is identified using sensitivity analysis performed on the decision-support based system or consensus thereof.

67. The method of claim 63, wherein the first decision-support system includes at least one neural network.

68. The method of claim 63, wherein the second decision-support system includes at least one neural network.

69. The method of claim 63, wherein the step of selecting a subset of selected important variables includes:

(i) providing a first set of n candidate variables and a second set of selected important variables, wherein the second set is initially empty;

(ii) taking candidate variable one at a time and evaluating each by training a decision-support system on that variable combined with the current set of selected important variable;

(iii) selecting the best of the candidate variables, wherein the best variable is any one that gives the highest performance of the decision-support system, and if the best candidate variable improves performance compared to the performance of the selected important variables, adding it to the selected important variable set, removing it from the candidate set and continuing evaluating at step (ii), wherein the best candidate variable does not improve performance, the process is completed.

70. The method of claim 63, wherein the step of selecting a subset of selected important variable includes:

(i) providing a first set of n candidate variable and a second set of selected important variables, wherein the second set is initially empty;

(ii) ranking all candidate variables, wherein the ranking is either arbitrary or ordered;

(iii) taking the highest m ranked variables one at a time, wherein m is from 1 up to n, and evaluating each by training a decision-support system on that variable combined with the current set of selected important variables;

(iv) selecting the best of the m variables, wherein the best variable is the one that gives the highest performance of the decision-support system, and if the best variable improves performance compared to the performance of the selected important variables, adding it to the selected important variable set, removing it from the candidate set and continuing evaluating at step (iii), if the variable does not improve performance in comparison to the performance of the selected important variables, evaluating is continued at step (v);

(v) determining if all variables on the candidate set have been evaluated, wherein if they have been evaluated, the process is complete and the set of selected important variables is a completed set, otherwise continuing by taking the next highest m ranked variables one at a time, and evaluating each by training a decision-support-system on that variable combined with the current set of important selected variables and performing step (iv).

71. The method of claim 70, wherein the candidate variables include biochemical test data.

72. The method of claim 70, wherein ranking is based on an analysis comprising a sensitivity analysis or other decision-support system-based analysis.

73. The method of claim 72, wherein the sensitivity analysis, comprises:

(i) determining an average observation value for each of the variables in the observation data set;

(ii) selecting a training example, and running the example through a decision-support system to produce an output value, designated and stored as the normal output;

(iii) selecting a first variable in the selected training example, replacing the observation value with the average observation value of the first variable, running the modified example in the decision-support system in the forward mode and recording the output as the modified output;

(iv) squaring the difference between the normal output and the modified output and accumulating it as a total, wherein the total for each variable designated the selected variable total for each variable;

(v) repeating steps (iii) and (iv) for each variable in the example;

(vi) repeating steps (ii)-(v) for each example in the data set, wherein each total for the selected variable represents the relative contribution of each variable to the determination of the decision-support system output.

74. The method of claim 70, wherein ranking is based on process comprising a statistical analysis.

75. The method of claim 70, wherein ranking is based on a process comprising chi square, regression analysis or discriminant analysis.

76. The method of claim 70, wherein ranking is determined by a process that uses evaluation by an expert, a rule based system, a sensitivity analysis or combinations thereof.

77. The method of claim 76, wherein the sensitivity analysis comprises:

(i) determining an average observation value for each of the variables in the observation data set;

(ii) selecting a training example, and running the example through a decision-support system to produce an output value, designated and stored as the normal output;

(iii) selecting a first variable in the selected training example, replacing the observation value with the average observation value of the first variable, running the modified example in the decision-support system in the forward mode and recording the output as the modified output;

(iv) squaring the difference between the normal output and the modified output and accumulating it as a total, wherein the total for each variable designated the selected variable total for each variable;

(v) repeating steps (iii) and (iv) for each variable in the example;

(vi) repeating steps (ii)-(v) for each example in the data set, wherein each total for the selected variable represents the relative contribution of each variable to the determination of the decision-support system output.

78. The method of claim 77, further comprising:

(vii) ranking the variables according to their relative contribution to the determination of the decision-support system output.

79. The method of claim 63, wherein the step of training a second decision-support system includes a validating step wherein a previously-unused set of observation data is run through the second decision-support system after training to provide a performance estimate for indication of the medical condition, wherein the previously-unused set of observation data are collected from patients in whom the medical condition is known.

80. The method of claim 63, wherein the step of training a second decision-support system includes partitioning the observation data set into a plurality of partitions comprising at least one testing data partition and a plurality of training data partitions, wherein the second decision-support system is run using the plurality of training data partitions and the testing data partition is used to provide a final performance estimate for the second decision-support system after the training data partitions have been run.

81. The method of claim 80, wherein the second decision-support system comprises a plurality of neural networks, each neural network of the plurality having a unique set of starting weights and having a performance rating value.

82. The method of claim 81, wherein the final performance estimate is generated by averaging the performance rating values for the plurality of neural networks.

83. The method of claim 63, wherein the observation values are obtained from patient historical data results and/or biochemical test results.

84. The method of claim 63, wherein the condition is a pregnancy-related condition or endometriosis.

85. The method of claim 63, further comprising:

(e) collecting additional observations from patients and categorizing them into a set of candidate variables, which are then added to first set of candidate variables; and

then

(f) repeating steps (c) and (d).

86. The method of claim 63, further comprising developing a diagnostic biochemical test for the identified biochemical test data variable(s).

87. A method for developing new biochemical tests or identifying new disease markers, comprising:

performing the method of claim 63, and identifying biochemcial data variables that are selected important variables;

and developing tests that detect the biochemical data or disease marker from which the variable is derived.

88. The method of claim 60, wherein the first subset of relevant variables is identified using sensitivity analysis performed on the decision-support based system or consensus thereof.

89. The method of claim 60, wherein the first decision-support system includes at least one neural network.

90. The method of claim 60, wherein the second decision-support system includes at least one neural network.

91. The method of claim 60, wherein the step of selecting a subset of selected important variable includes:

(i) providing a first set of n candidate variables and a second set of selected important variables, wherein the second set is initially empty;

(ii) taking candidate variables one at a time and evaluating each by training a decision-support system on that variable combined with the current set of selected important variables.

(iii) selecting the best of the candidate variables, wherein the best variable is any one that gives the highest performance of the decision-support support system, and if the best candidate variable improves performance compared to the performance of the selected important variables, adding it to the selected important variable set, removing it from the candidate set and continuing evaluating at step (ii), wherein the best candidate variable does not imporive performance, the process is completed.

92. The method of claim 60, wherein the step of selecting a subset of selected important variables includes:

(i) providing a first set of n candidate variables and a second set of selected important variables, wherein the second set is initially empty;

(ii) ranking all candidate variables, wherein the ranking is either arbitrary or ordered;

(iii) taking the highest m ranked variables one at a time, wherein m is from 1 up to n, and evaluating each by training a decision-support system on that variable combined with the current set of selected important variables;

(iv) selecting the best of the m variables, wherein the best variable is the one that gives the highest performance of the decision-support system, and if the best variable improves performance compared to the performance of the selected important variables, adding it to the selected important variable set, removing it from the candidate set and continuing evaluating at step (iii), if the variable does not improve performance in comparsion to the performance of the selected important variables, evaluating is continued at step (v);

(v) determining if all variables on the candidate set have been evaluated, wherein if they have been evaluated, the process is complete and the set of selected important variables is a completed set, otherwise continuing by taking the next highest m ranked variables one at a time, and evaluating each by training a decision-support-system on that variable combined with the current set of important selected variables and performing step (iv).

93. The method of claim 92, wherein the candidate variables include biochemical test data.

94. The method of claim 93, wherein ranking is determined by a process that uses evaluation by an expert, a rule based system, a sensitivity analysis or combinations thereof.

95. The method of claim 94, wherein the sensitivity analysis comprises:

(i) determining an average observation value for each of the variables in the observation data set;

(ii) selecting a training example, and running the example through a decision-support system to produce an output value, designated and stored as the normal output;

(iii) selecting a first variable in the selected training example, replacing the observation value with the average observation value of the first variable, running the modified example in the decision-support system in the forward mode and recording the output as the modified output;

(iv) squaring the difference between the normal output and the modified output and accumulating it as a total, wherein the total for each variable designated the selected variable total for each variable;

(V) repeating steps (iii) and (iv) for each variable in the example;

(vi) repeating steps (ii)-(v) for each example in the data set, wherein each total for the selected variables represents the relative contribution of each variable to the determination of the decision-support system output.

96. The method of claim 95, further comprising:

(vii) ranking the variables according to their relative contribution to the determination of the decision-support system output.

97. The method of claim 92, wherein ranking is based on an analysis comprising a sensitivity analysis or other decision-support system-based analysis.

98. The method of claim 97, wherein the sensitivity analysis, comprises:

(i) determining an average observation value for each of the variables in the observation data set;

(ii) selecting a training example, and running the example through a decision-support system to produce an output value, designated and stored as the normal output;

(iii) selecting a first variable in the selected training example, replacing the observation value with the average observation value of the first variable, running the modified example in the decision-support system in the forward mode and recording the output as the modified output;

(iv) squaring the difference between the normal output and the modified output and accumulating it as a total, wherein the total for each variable designated the selected variable total for each variable;

(v) repeating steps (iii) and (iv) for each variable in the example;

(vi) repeating steps (ii)-(v) for each example in the data set, wherein each total for the selected variable represents the relative contribution of each variable to the determination of the decision-support system output.

99. The method of claim 92, wherein ranking is based on a process comprising a statistical analysis.

100. The method of claim 92, wherein ranking is based on a process comprising chi square, regression analysis or discriminant analysis.

101. The method of claim 60, wherein the step of training a second decision-support system includes a validating step wherein a previously-unused set of observation data is run through the second decision-support system after training to provide a performance estimate for indication of the medical condition, wherein the previously-unused set of observation data are collected from patients in whom the medical condition is known.

102. The method of claim 60, wherein the step of training a second decision-support system includes partitioning the observation data set into a pluarlity of partitions comprising at least one testing data partition and a plurality of training data partitions, wherein the second decision-support system is run using the plurality of training data partitions and the testing data partition is used to provide a final performance estimate for the second decision-support system after the training data partitions have been run.

103. The method of claim 102, wherein the second decision-support system comprises a plurality of neural networks, each neural network of the plurality having a unique set of starting weights and having a performance rating value.

104. The method of claim 103, wherein the final performance estimate is generated by averaging the performance rating values for the plurality of neural networks.

105. The method of claim 60, wherein the observation values are obtained from patient historical data results and/or biochemical test results.

106. The method of claim 60, wherein the condition is a pregnancy-related condition or endometriosis.

107. The method of claim 60, further comprising:

(e) collecting additional observations from patients and categorizing them into a set of candidate variables, which are then added to first set of candidate variables; and

then

(f) repeating steps (c) and (d).

108. The method of claim 38, wherein the computer-based decision-support system includes a system selected from the group consisting of expert system, fuzzy logic systems, non-linear regression analysis systems, multivariate analysis systems, decision tree classifiers, Bayesian belief networks and neural networks.

109. The method of claim 38, wherein the computer-based decision-support system comprises a neural network or plurality of neural networks.

110. A computer-based diagnostic test produced by the method of claim 38.

111. A computer readable medium or computer memory, comprising a decision-support system produced by the method of claim 38.

112. A computer memory or on a computer-readable medium produced by the method of claim 38.

113. The method of claim 38, wherein the disorder is endometriosis and the candidate variables comprise at least four of the variables selected from:

(i) past history of endometriosis, number of births, dysmenorrhea, age pelvic pain, history of pelvic surgery, smoking quantity per day, medication history, number of pregnancies, number of abortions, abnormal PAP/dysplasia, pregnancy hypertension, gential warts, and diabetes, or

(ii) age, parity, gravidity, number of abortions, smoking quantity per day, past history of endometriosis, dysmenorrhea, pelvic pain, abnormal PAP, history of pelvic surgery, medication history, pregnancy hypertension, genital warts and diabetes.

114. The method of claim 113, wherein the decision-support system comprises a neural network or a consensus of neural networks.

115. The method of claim 113, wherein at least five variables are selected.

116. A method for analyzing effectiveness of a diagnostic test to aid in diagnosing the presence, absence or severity of a medical condition or for assessing a course of treatment or the effectiveness of a particular treatment in a patient comprising:

(a) collecting observations from a group of test patients in whom the medical condition is known by querying and examining the test patients;

(b) categorizing the observations into a set of candidate variables having observation values and storing the observation values as a observation data set in a computer;

(c) selecting a subset of selected important variables from the set of candidate variables by classifying the observation data set using a first decision-support system programmed into the computer system; and

(d) training a second decision-support system using the observation data corresponding to the subset of selected important variables;

(e) performing the diagnostic test under analysis or treating test patients with the test treatment, wherein the test or treatment is performed before, during or after any of steps (a)-(d);

(f) collecting results of the diagnostic test under analysis or collecting observation after or during treatment from same the group of test patients;

(g) categorizing the observations into a second set of candidate variables having observation values, combining them with the observations from step (b), and storing the observation values as a observation data set in a computer;

(h) selecting a second subset of selected important variables by classifying the observation data set using a first decision-support system programmed into the computer system;

(i) training a third decision-support system using the observation data corresponding to a subset of selected important variables from step (g); and

(j) comparing the performance of the second and third systems, thereby assessing the effectiveness of a disgnostic test to aid in diagnosing the presence, absence or severity of a medical condition or assessing the effectiveness of a course of treatment or the effectiveness of a particular treatment in treating a disease, disorder or condition.

117. The method of claim 116, wherein the method assesses the effectiveness of a diagnostic test in aiding in a diagnosis.

118. The method of claim 116, wherein the method assesses the presence, absence, severity or course of treatment of a disease, disorder or other medical condition or aids in determining the outcome resulting from a selected treatment.

119. The method of claim 116, wherein the decision-support system includes a neural network and the final set constitutes a consensus of neural networks.

120. The method of claim 116, wherein the first subset of relevant variables is identified using sensitivity analysis performed on the decision-support based system or consensus thereof.

121. The method of claim 116, wherein the first decision-support system includes at least one neural network.

122. The method of claim 116, wherein the second decision-support system includes at least one neural network.

123. The method of claim 116, wherein the third decision-support system includes at least one neural network.

124. The method of claim 116, wherein the step of selecting a subset of selected important variables includes:

(i) providing a first set of n candidate variables and a second set of selected improtant variables, wherein the second set is initially empty;

(ii) taking candidate variables one at a time and evaluating each by training a decision-support system on that variable combined with the current set of selected important variables;

(iii) selecting the best of the candidate variables, wherein the best variable is any one that gives the highest performance of the decision-support system, and if the best candidate variable improves performance compared to the performance of the selected important variables, adding it to the selected important variable set, removing it from the candidate set and continuing evaluating at step (ii), wherein the best candidate variable does not improve performance, the process is completed.

125. The method of claim 116, wherein the step of selecting a subset of selected important variables includes:

(i) providing a first set of n candidate variables and a second set of selected important variables, wherein the second set is initially empty;

(ii) ranking all candidate variables, wherein the ranking is either arbitrary or ordered;

(iii) taking the highest m ranked variables one at a time, wherein m is from 1 up to n, and evaluating each by training a decision-support system on that variable combined with the current set of selected important variables;

(iv) selecting the best of the m variables, wherein the best variable is the one that gives the highest performance of the decision-support system, and if the best variable improves performance compared to the performance of the selected important variables, adding it to the selected important variable set, removing it from the candidate set and continuing evaluating at step (iii), if the variable does not improve performance in comparison to the performance of the selected important variables, evaluating is continued at step (v);

(v) determining if all variables on the candidate set have been evaluated, wherein if they have been evaluated, the process is complete and the set of selected important variables is a completed set, otherwise continuing by taking the next highest m ranked variables one at a time, and evaluating each by training a decision-support-system on that variable combined with the current set of important selected variables and performing step (iv).

126. The method of claim 125, wherein ranking is based on an analysis comprising a sensitivity analysis or other decision-support system-based analysis.

127. The method of claim 126, wherein ranking is determined by a process that uses evaluation by an expert, a rule based system, a sensitivity analysis or combinations thereof.

128. The method of claim 127, wherein the sensitivity analysis comprises:

(i) determining an average observation value for each of the variables in the observation data set;

(ii) selecting a training example, and running the example through a decision-support system to produce an output value, designated and stored as the normal output;

(iii) selecting a first variable in the selected training example, replacing the observation value with the average observation value of the first variable, running the modified example in the decision-support system in the forward mode and recording the output as the modified output;

(iv) squaring the difference between the normal output and the modified output and accumulating it as a total, wherein the total for each variable designated the selected variable total for each variable;

(v) repeating steps (iii) and (iv) for each variable in the example;

(vi) repeating steps (ii)-(v) for each example in the data set, wherein each total for the selected variable represents the relative contribution of each variable to the determination of the decision-support system output.

129. The method of claim 128, further comprising:

(vii) ranking the variables according to their relative contribution to the determination of the decision-support system output.

130. The method of claim 126, wherein the sentivity analysis, comprises:

(i) determining an average observation value for each of the variables in the observation data set;

(ii) selecting a training example, and running the example through a decision-support system to produce an output value, designated and stored as the normal output;

(iii) selecting a first variable in the selected training example, replacing the observation value with the average observation value of the first variable, running the modified example in the decision-support system in the forward mode and recording the output as the modified output;

(iv) squaring the difference between the normal output and the modified output and accumulating it as a total, wherein the total for each variable designated the selected variable total for each variable;

(v) repeating steps (iii) and (iv) for each variable in the example;

(vi) repeating steps (ii)-(v) for each example in the data set, wherein each total for the selected variable represents the relative contribution of each variable to the determination of the decision-support system output.

131. The method of claim 125, wherein ranking is based on process comprising a statistical analysis.

132. The method of claim 125, wherein ranking is based on a process comprising chi square, regression analysis or discriminant analysis.

133. The method of claim 116, wherein the step of training a second and/or third decision-support system includes a validating step wherein a previously-unused set of observation data is run through the second decision-support system after training to provide a performance estimate for indication of the medical condition, wherein the previously-unused set of observation data are collected from patients in whom the medical condition is known.

134. The method of claim 116, wherein the step of training a second and/or third decision-support system includes partitioning the observation data set into a plurality of partitions comprising at least one testing data partition and a plurality of training data partitions, wherein the second decision-support system is run using the plurlaity of training data partitions and the testing data partition is used to provide a final performance estimate for the second decision-support system after the training data partitions have been run.

135. The method of claim 134, wherein the each decision-support system comprises a plurality of neural networks, each neural network of the plurality having a unique set of weights and having a performance rating value.

136. The method of claim 135, wherein the final performance estimate is generated by averaging the performance rating values for the plurality of neural networks.

137. The method of claim 116, wherein the observation values are obtained from patient historical data results and/or biochemical test results.

138. The method of claim 116, wherein the condition is a pregnancy-related condition or endometriosis.

139. The method of claim 116, further comprising:

collecting additional observations from patients and categorizing them into a set of candidate variables, which are than added to first set of candidate variable at step (b); and

then

(j) repeating steps (c)-(i).

140. A method for developing a condition-specific biochemical test to aid in diagnosing the presence, absence, or severity of a medical condition in a patient comprising:

(a) performing and collecting test results of a biochemcial test from a group of test patients in whom the condition is known or suspected;

(b) categorizing the test results and other observations into a set of candidate variables having observation values and storing the observation values as an observation data set in a computer;

(c) selecting a subject of selected important variables from a set of variables comprising the candidate variables by classifying the observation data set using a first decision-support system programmed into the computer system, whereby the subset of selected important variables includes the candidate variables substantially indicative of the medical condition; and

(d) identifying those variable(s) in the selected important variable set that correspond to biochemical data; and

(e) producing a biochemical test that assesses the data that corresponds to the identified variable(s).

141. The method of claim 140, wherein the decision-support system includes neural network or consensus thereof.

142. The method of claim 140, wherein the test assesses the presence, absence, severity or course of treatment of a disease, disorder or other medical condition or aids in determining the outcome resulting from a selected treatment.

143. The method of claim 140, wherein the decision-support system includes a neural network and the final set constitutes a consensus of neural networks.

144. The method of claim 140, wherein the first subset of relevant variables is identified using sensitivity analysis performed on the decision-support based system or consensus thereof.

145. The method of claim 140, wherein the first decision-support system includes at least one neural network.

146. The method of claim 140, wherein the second decision-support system includes at least one neural network.

147. The method of claim 140, wherein the step of selecting a subset of selected important variables includes:

(i) providing a first set of n candidate variables and a second set of selected important variables, wherein the second set is initially empty;

(ii) taking candidate variables one at a time and evaluating each by training a decision-support system on that variable combined with the current set of selected important variables;

(iii) selecting the best of the candidate variables, wherein the best variable is any one that gives the highest performance of the decision-support system, and if the best candidate variable improves performance compared to the performance of the selected important variables, adding it to the selected important variable set, removing ot from the candidate set and continuing evaluating at step (ii), wherein the best candidate variable does not improve performance, the process is completed.

148. The method of claim 140, wherein the step of selecting a subset of selected important variables includes:

(i) providing a first set of n candidate variables and a second set of selected important variables, wherein the second set is initially empty;

(ii) ranking all candidate variables, wherein the ranking is either arbitrary or ordered;

(iii) taking the highest m ranked variables one at a time, wherein m is from 1 up to n, and evaluating each by training a decision-support system on that variable combined with the current set of selected important variables;

(iv) selecting the best of the m variables, wherein the best variable is the one that gives the highest performance of the decision-support system, and if the best variable improves performance compared to the performance of the selected important variables, adding it to the selected important variable set, removing it from the candidate set and continuing evaluating at step (iii), if the variable does not improve performance in comparison to the performance of the selected important variables, evaluating is continued at step (v);

(v) determining if all variables on the candidate set have been evaluated, wherein if they have been evaluated, the process is completed and the set of selected important variables is a completed set, otherwise continuing by taking the next highest m ranked variables one at a time, and evaluating each by training a decision-support-system on that variable combined with the current set of important selected variables and performing step (iv).

149. The method of claim 148, wherein the candidate variables include biochemical test data.

150. The method of claim 149, wherein ranking is determined by a process that uses evaluation by an expert, a rule based system, a sensitivity analysis or combinations thereof.

151. The method of claim 150, wherein the sensitivity analysis comprises:

(i) determining an average observation value for each of the variable in the observation data set;

(ii) selecting a training example, and running the example through a decision-support system to produce an output value, designated and stored as the normal output;

(iii) selecting a first variable in the selected training example, replacing the observatio value with the average observation value of the first variable, running the modified example in the decision-support system in the forward mode and recording the output as the modified output;

(iv) squaring the difference between the normal output and the modified output and accumulating it as a total, wherein the total for each variable designated the selected variable total for each variable;

(v) repeating steps (iii) and (iv) for each variable in the example;

(vi) repeating steps (ii)-(v) for each example in the data set, wherein each total for the selected variable represents the relative contribution of each variable to the determination of the decision-support system output.

152. The method of claim 151, further comprising:

(vii) ranking the variables according to their relative contribution to the determination of the decision-support system output.

153. The method of claim 148, wherein ranking is based on an analysis comprising a sensitivity analysis or other decision-support system-based analysis.

154. The method of claim 153, wherein the sensitivity analysis, comprises:

(i) determining an average observation value for each of the variables in the observation data set;

(ii) selecting a training example, and running the example through a decision-support system to produce an output value, designated and stored as the normal output;

(iii) selecting a first variable in the selected training example, replacing the observation value with the average obseration value of the first variable, running the modified example in the decision-support system in the forward mode and recording the output as the modified output;

(iv) squaring the difference between the normal output and the modified output and accumulating it as a total, wherein the total for each variable designated the selected variable total for each variable;

(v) repeating steps (iii) and (iv) for each variable in the example;

(vi) repeating steps (ii)-(v) for each example in the data set, wherein each total for the selected variable represents the relative contribution of each variable to the determination of the decision-support system output.

155. The method of claim 148, wherein ranking is based on a process comprising a statistical analysis.

156. The method of claim 148, wherein ranking is based on a process comprising chi square, regression analysis or discriminant analysis.

157. The method of claim 140, wherein the step of training a second decision-support system includes a validating step wherein a previously-unused set of observation data is run through the second decision-support system after training to provide a performance estimate for indication of the medical condition, wherein the previously-unused set of observation data are collected from patients in whom the medical condition is known.

158. The method of claim 140, wherein the step of training a second decision-support system includes partitioning the observation data set into a plurality of partitions comprising at least one testing data partition and a plurality of training data partitions, wherein the second decision-support system is run using the plurality of training data partitions and the testing data partition is used to provide a final performance estimate for the second decision-support system after the training data partitions have been run.

159. The method of claim 158, wherein the second decision-support system comprises a plurality of neural networks, each neural network of the plurality having a unique set of starting weights and having a performance rating value.

160. The method of claim 159, wherein the final performance estimate is generated by averaging the performance rating values for the plurality of neural networks.

161. The method of claim 140, wherein the observation values are obtained from patient historical data results and/or biochemcial test results.

162. The method of claim 140, wherein the condition is a pregnancy-related condition or endometriosis.

163. The method of claim 140, further comprising:

(e) collecting additional observations from patients and categorizing them into a set of candidate variables, which are then added to first set of candidate variables; and

then

(f) repeating steps (c) and (d).

164. A method for diagnosing endometriosis in a subject, comprising:

querying and examining the patient to assess at least three of the following variables;

Past history of endometriosis,

number of births,

dysmenorrhea,

age preprocessed to be normalized to a value between 1 and 0,

pelvic pain,

history of pelvic surgery,

smoking and if yes, the number of packs/day

medication history,

number of pregnancies,

number of abortions,

Abnormal PAP smear/dysplasia,

Pregnancy hyperplasia,

Genital Warts,

Diabetes,

entering the results of the queries and examination into a computer system that comprises a computer-based

decision-support system that has been trained to diagnose endometriosis; and

producing an output indicative of a diagnosis.

165. The method of claim 164, wherein the selected subset of these variables contains one or more of the following combinations of three variables set forth in sets (a)-(n):

a) number of births, history of endometriosis, history of pelvic surgery;

b) diabetes, pregnancy hypertension, smoking;

c) pregnancy hypertension, abnormal pap smear/dysplasia, history of endometriosis;

d) age, smoking, history of endometriosis;

e) smoking, history of endometriosis, dysmenorrhea;

f) age, diabetes, history of endometriosis;

g) pregnancy hypertension, number of births, history of endometriosis;

h) Smoking, number of births, history of endometriosis;

i) pregnancy hypertension, history endometriosis, history of pelvic surgery;

j) number of pregnancies, history of endometriosis, history of pelvic surgery;

k) number of births, abnormal PAP smear/dysplasia, history of endometriosis;

l) number of births, abnormal PAP smear/dysplasia, dysmenorrhea;

m) history of endometriosis, history of pelvic surgery, dysmenorrhea; and

n) number of pregnancies, history of endometriosis, dysmenorrhea.

166. The method of claim 165, wherein the decision support system is a neural network.

167. A method to aid in diagnosis of the presence, absence or severity of endometriosis in a patient comprising:

(a) querying and examining the patient to collect observation values reflecting presence and absence of specified clinical data factors and storing the observed clinical data factors in storage means of the computer system, the specified clinical data factors comprising at least four of the factors selected from;

(i) past history of endometriosis, number or births, dysmenorrhea, age, pelvic pain, history of pelvic surgery, smoking quantity per day, medication history, number of pregnancies, number of abortions, abnormal PAP/dysplasia, pregnancy hypertension, genital warts, and diabetes, or

(ii) age, parity, gravidity, number of abortions, smoking quantity per day, past history of endometriosis, dysmenorrhea, pelvic pain, abnormal PAP, history of pelvic surgery, medication history, pregnancy hypertension, genital warts and diabetes;

(b) applying the observation values from the memory means to a first computer-based decision-support system trained on samples of the specified factors; and

thereupon

(c) extracting from the first decision-support system an output value, wherein the output value is a quantitative objective aid to enhance decision processes for a diagnosis of endometriosis.

168. The method of claim 167, wherein the decision-support system comprises a neural network.

169. The method of claim 167, wherein at least five factors are selected.

170. The method of claim 167, further comprising:

b1) applying said observation values from said memory means to a plurality of the first decision-support system, wherein each one of the first decision-support systems is trained on the samples of the specified factors with different starting weights for each training;

c1) extracting from the first decision-support system, output value pairs for each one of said first neural networks; and

d) forming a linear combination of said first ones of said output value pairs and forming a linear combination of said second ones of said output value pairs, to obtain a confidence index pair, said confidence index pair being said quantitative objective aid.

171. The method of claim 170, wherein the first decision support system is a neural network that comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having fourteen input nodes, first and second hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

172. The method of claim 170, wherein the first decision support system is a neural network and each of the plurality of first trained neural networks comprises a three-layer network comprising an input layer, a hidden layer and an output layer.

173. A decision support system is a neural network that comprises a three-layer neural network, comprising an input layer, a hidden layer and an output layer, the input layer having fourteen input nodes, first and second hidden layer nodes and a hidden layer bias for each hidden layer node and first and second output layer nodes in the output layer and an output layer bias for each output layer node,

wherein weights, in order of identification as follows:

0. Bias

1. Age

2. Diabetes

3. Pregnancy hypertension

4. Smoking Packs/Day

5. Number of Pregnancies

6. Number of Births

7. Number of Abortions

8. Genital Warts

9. Abnormal PAP/Dysplasia

10. History of Endometriosis

11. History of Pelvic Surgery

12. Medication History

13. Pelvic Pain

14. Dysmenorrhea

are as follows for each of eight neural networks of the first neural networks;

First neural network A

to processing element at the first hidden layer node;

0.15 −

1.19 −

0.76 3.01 1.81 1.87 3.56 −

0.48 1.33 −

1.96 −

4.45 1.36 −

1.61 −

1.97 −

0.91

to processing element at the second hidden layer node;

0.77 2.25 −

2.30 −

1.48 −

0.85 0.27 −

1.70 −

0.47 0.84 −

6.19 0.50 −

0.95 0.40 2.38 1.86

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

−

0.12 −

0.44 0.66

to processing element at the second output layer node;

0.12 0.44 −

0.65

First neural network B

to processing element at the first hidden layer node;

−

0.16 −

3.30 0.85 1.00 0.99 −

0.81 1.57 −

1.40 0.46 1.16 −

0.80 −

0.01 −

1.19 −

1.10 −

2.29

to processing element at the second hidden layer node;

−

1.62 0.79 0.45 2.14 3.82 3.93 3.96 2.27 −

0.54 1.51 −

4.76 2.83 0.74 −

0.43 −

0.17

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

0.70 −

0.69 −

0.65

to processing element at the second output layer node;

−

0.70 0.69 0.65

First neural network C

to processing element at the first hidden layer node;

0.94 1.43 0.29 1.17 2.11 −

1.16 1.033 −

0.68 −

0.88 0.31 −

1.74 1.62 −

1.49 −

1.05 −

0.41

to processing element at the second hidden layer node;

0.77 3.31 −

1.48 −

0.83 0.60 −

2.09 −

1.39 −

0.40 −

0.19 −

0.89 1.36 0.59 −

1.11 0.26 1.04

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

0.10 −

0.90 0.87

to processing element at the second output layer node;

−

0.10 0.90 −

0.87

First neural network D

to processing element at the first hidden layer node;

1.08 1.27 −

0.89 −

1.00 −

1.74 −

0.40 −

1.38 1.26 1.06 0.66 0.71 −

0.57 0.67 1.89 −

0.90

to processing element at the second hidden layer node;

−

0.03 −

0.58 −

0.46 −

0.94 0.73 0.10 0.55 −

0.79 −

0.098 −

1.36 1.01 0.00 −

0.38 −

0.49 1.57

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

−

1.43 1.39 1.28

to processing element at the second output layer node;

1.30 −

1.28 −

1.17

First neural network E

to processing element at the first hidden layer node;

0.14 −

2.12 8.36 1.02 1.79 0.31 2.87 0.84 −

1.24 −

1.75 −

2.98 1.72 −

1.22 −

2.47 −

1.14

to processing element at the second hidden layer node;

−

3.93 −

1.07 1.16 1.39 1.01 −

1.08 2.33 0.76 −

0.51 −

0.31 −

1.92 0.59 0.06 −

0.76 −

1.44

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

0.46 −

0.52 −

0.80

to processing element at the second output layer node;

−

0.46 0.51 0.82

First neural network F

to processing element at the first hidden layer node;

−

1.19 −

2.93 1.19 6.85 1.08 0.66 1.65 −

0.28 −

1.63 −

1.15 −

0.79 0.43 −

0.13 −

3.10 −

2.27

to processing element at the second hidden layer node;

0.82 0.19 0.72 0.83 0.59 0.07 1.06 0.51 1.04 1.47 −

1.97 0.97 −

0.91 −

0.15 0.09

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

0.68 −

0.67 −

0.58

to processing element at the second output layer node;

−

0.68 0.67 0.58

First neural network G

to processing element at the first hidden layer node;

−

1.18 −

2.55 0.48 −

1.40 1.11 −

0.28 2.33 0.33 −

1.92 0.99 −

1.41 0.68 −

0.28 −

1.65 −

0.79

to processing element at the second hidden layer node;

1.07 1.11 0.52 1.41 0.55 −

0.48 −

0.23 0.44 −

1.23 0.77 −

2.96 1.39 −

0.28 −

0.64 −

2.38

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

0.69 −

0.70 −

0.50

to processing element at the second output layer node;

−

0.69 0.70 0.50

First neural network H

to processing element at the first hidden layer node;

15.74 −

0.76 −

0.91 −

1.13 −

0.75 −

0.66 −

0.83 1.03 0.75 −

0.48 −

0.47 2.01 −

0.02 0.25 1.11

to processing element at the second hidden layer node;

−

2.48 −

2.49 0.99 1.97 2.41 1.51 1.01 −

0.26 −

0.76 2.00 −

5.03 1.77 −

0.77 −

2.29 −

2.01

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

0.02 0.41 −

0.84

to processing element at the second output layer node;

−

0.75 0.34 0.85.

174. The network of claim 173, wherein normalized observation values for each one of the first neural networks have the following mean and standard deviations, in order of the identification:

−

0.00 1.00

0.01 0.08

0.01 0.09

0.16 0.37

1.09 1.39

0.55 0.94

0.54 0.93

0.01 0.10

0.03 0.17

0.23 0.42

0.65 0.48

0.39 0.49

0.19 0.39

0.72 0.45.

175. In a computer system, a method to aid in diagnosis of the presence, absence or severity of endometriosis in a patient comprising the steps of:

(a) collecting observation values reflecting presence and absence of specified factors and storing the observation factors in storage means of the computer system, the specified factors comprising;

past history of the disease, number of births, dysmenorrhea, age, pelvic pain, history of pelvic surgery, smoking quantity per day, medication history, number of pregnancies, number of abortions, abnormal PAP/dysplasia, pregnancy hypertension, genital warts, and diabetes;

(b) obtaining results from the patient of a biochemical test relevant to endometriosis and storing in a computer memory or computer storage medium;

(c) applying the observation values and the relevant biochemical test results from the memory or storage medium to a neural network trained on samples of the specified factors and the test results; and

thereupon

(d) extracting from the trained neural network and output value pair, the output value pair being a preliminary indicator for the diagnosis of endometriosis.

176. The method of claim 175, further including the steps of:

(c1) applying the observation values and the relevant biochemical test results from the memory means to a plurality of the second neural networks, each one of the first neural networks being trained on the samples of the specified factors with starting weights for each training being randomly initialized;

(d1) extracting from each one of the first trained neural networks, output value pairs for each one of the first neural networks; and

(e) forming a linear combination of the first ones of the output value pairs and forming a linear combination of the second ones of the output value pairs, to obtain a confidence index pair, the confidence index pair being a final indicator for the diagnosis of endometriosis.

177. The method of claim 176, wherein the plurality of the second trained neural networks each comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having fifteen input nodes, first and second hidden layer nodes and a hidden layer bias for each hidden layer node and first and second output layer nodes in the output layer and an output layer bias for each output layer node,

wherein weights, in order of identification as follows:

0. Bias

1. Age

2. Diabetes

3. Pregnancy hypertension

4. Smoking Packs/Day

5. Number of Pregnancies

6. Number of Births

7. Number of Abortions

8. Genital Warts

9. Abnormal PAP/Dysplasia

10. History of Endometriosis

11. History of Pelvic Surgery

12. Medication History

13. Pelvic Pain

14. Dysmenorrhea

15. Biochemical test results.

178. The method of claim 175, wherein the first trained neural network comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having fifteen input nodes, first and second hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

179. In a computer system, a neural network system to aid in diagnosis of the presence, absence or severity of endometriosis in a patient, the neural network system comprising:

a plurality of first trained neural networks each comprising a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having fourteen input nodes, first and second hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node, each trained neural network for generating a preliminary indicator for the diagnosis of endometriosis;

input means for observed values of clinical data factors;

storage means of the computer system for the observed values of the clinical data factors, the clinical data factors comprising;

past history of the disease, number of births, dysmenorrhea, age, pelvic pain, history of pelvic surgery, smoking quantity per day, medication history, number of pregnancies, number of abortions, abnormal PAP/dysplasia, pregnancy hypertension, genital warts, and diabetes; and

means for building a consensus from the output layer nodes, the consensus being a quantitative objective aid to enhance the decision process for the diagnosis of endometriosis.

180. The neural network system of claim 179, further including:

an input normalizer for normalizing the observation values from the memory means to a plurality of the first neural networks, each one of the first neural networks being trained on the samples of the specified factors with starting weights for each training being randomly initialized.

181. The neural network system of claim 179, wherein the consensus builder comprises a linear combiner of first ones of output value pairs and of second ones of output value pairs, to obtain a confidence index pair, the confidence index pair being the consensus and final indicator for the diagnosis of endometriosis.

182. The neural network system of claim 179, wherein weights, in order of identification as follows:

0. Bias

1. Age

2. Diabetes

3. Pregnancy hypertension

4. Smoking Packs/Day

5. Number of Pregnancies

6. Number of Births

7. Number of Abortions

8. Genital Warts

9. Abnormal PAP/Dysplasia

10. History of Endometriosis

11. History of Pelvic Surgery

12. Medication History

13. Pelvic Pain

14. Dysmenorrhea

are as follows for each of eight the first neural networks;

First neural network A

to processing element at the first hidden layer node;

0.15 −

1.19 −

0.76 3.01 1.81 1.87 3.56 −

0.48 1.33 −

1.96 −

4.45 1.36 −

1.61 −

1.97 −

0.91

to processing element at the second hidden layer node;

0.77 2.25 −

2.30 −

1.48 −

0.85 0.27 −

1.70 −

0.47 0.84 −

6.19 0.50 −

0.95 0.40 2.38 1.86

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

−

0.12 −

0.44 0.66

to processing element at the second output layer node;

0.12 0.44 −

0.65

First neural network B

to processing element at the first hidden layer node;

−

0.16 −

3.29 0.85 1.00 0.99 −

0.81 1.57 −

1.40 0.46 1.16 −

0.80 −

0.01 −

1.19 −

1.10 −

2.29

to processing element at the second hidden layer node;

−

1.62 0.79 0.45 2.14 3.82 3.93 3.96 2.27 −

0.54 1.51 −

4.76 2.83 0.74 −

0.43 −

0.17

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

0.70 −

0.69 −

0.65

to processing element at the second output layer node;

−

0.70 0.69 0.65

First neural network C

to processing element at the first hidden layer node;

0.94 1.43 0.29 1.17 2.11 −

1.16 1.03 −

0.68 −

0.88 0.31 −

1.74 1.62 −

1.49 −

1.05 −

0.41

to processing elemept at the second hidden layer node;

0.77 3.31 −

1.48 −

0.83 0.60 −

2.09 −

1.39 −

0.40 −

0.19 −

0.89 1.36 0.59 −

1.11 0.26 1.04

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

0.10 −

0.90 0.87

to processing element at the second output layer node;

−

0.10 0.90 −

0.87

First neural network D

to processing element at the first hidden layer node;

1.08 1.27 −

0.89 −

1.00 −

1.74 −

0.40

−

1.38 1.26 1.06 0.66 0.71 −

0.57 0.67 1.89 −

0.90

to processing element at the second hidden layer node;

−

0.03 −

0.58 −

0.46 −

0.94 0.73 0.10 0.55 −

0.79 −

0.10 −

1.36 1.01 0.00 −

0.38 −

0.49 1.57

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

−

1.43 1.39 1.28

to processing element at the second output layer node;

1.30 −

1.28 −

1.17

First neural network E

to processing element at the first hidden layer node;

0.14 −

2.12 8.36 1.02 1.79 0.31 2.87 0.84 −

1.24 −

1.75 −

2.98 1.72 −

1.22 −

2.47 −

1.14

to processing element at the second hidden layer node;

−

3.93 −

1.07 1.16 1.39 1.01 −

1.08 2.33 0.76 −

0.51 −

0.31 −

1.92 0.59 0.06 −

0.76 −

1.44

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

0.46 −

0.52 −

0.80

to processing element at the second output layer node;

−

0.46 0.51 0.82

First neural network F

to processing element at the first hidden layer node;

−

1.19 −

2.93 1.19 6.85 1.08 0.66 1.65 −

0.28 −

1.63 −

1.15 −

0.79 0.43 −

0.13 −

3.10 −

2.27

to processing element at the second hidden layer node;

0.82 0.19 0.72 0.83 0.59 0.07 1.06 0.51 1.04 1.47 −

1.97 0.97 −

0.91 −

0.15 0.09

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

0.68 −

0.67 −

0.58

to processing element at the second output layer node;

−

0.68 0.670.58

First neural network G

to processing element at the first hidden layer node;

−

1.18 −

2.55 0.48 −

1.40 1.11 −

0.28 2.33 0.33 −

1.92 0.99 −

1.41 0.68 −

0.28 −

1.65 −

0.79

to processing element at the second hidden layer node;

1.08 1.11 0.52 1.41 0.55 −

0.48 −

0.23 0.44 −

1.23 0.77 −

2.96 1.39 −

0.28 −

0.64 −

2.38

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

0.69 −

0.70 −

0.50

to processing element at the second output layer node;

−

0.69 0.70 0.50

First neural network H

to processing element at the first hidden layer node;

15.74 −

0.76 −

0.91 −

1.13 −

0.75 −

0.66 −

0.83 1.03 0.75 −

0.48 −

0.47 2.01 −

0.02 0.25 1.11

to processing element at the second hidden layer node;

−

2.48 −

2.49 0.99 1.97 2.41 1.51 1.01 −

0.26 −

0.76 2.00 −

5.03 1.77 −

0.77 −

2.29 −

2.01

output layer weights (0 bias, first hidden layer weight, second hidden layer weight)

to processing element at the first output layer node;

0.017 0.41 −

0.84

to processing element at the second output layer node;

−

0.75 0.34 0.85.

183. The system of claim 182, wherein normalized observation values for each one of the first neural networks have the following mean and standard deviations, in order of the identification:

−

0.00 1.00

0.01 0.08

0.01 0.09

0.16 0.37

1.09 1.39

0.55 0.94

0.54 0.93

0.01 0.10

0.03 0.17

0.23 0.42

0.65 0.48

0.39 0.49

0.19 0.39

0.72 0.45.

184. The system of claim 179, further comprising storage means for biochemical results, and wherein the plurality of networks have been trained to include biochemical test results.

185. A method for assessing the risk of delivery prior to completion of 35 weeks of gestation, comprising assessing a subset of variables containing at least three and up to all of the following variables:

Ethnic Origin Caucasian;

Marital Status living with partner;

EGA by sonogram;

EGA at sampling;

estimated date of delivery by best;

cervical dilatation (CM);

parity-preterm;

vaginal bleeding at time of sampling;

cervical consistency at time of sampling; and

previous pregnancy without complication, by querying and testing the subject;

entering the results of the queries and tests into a computer system that comprises a decision-support system that has been trained to assesses the risk of delivery prior to 35 weeks of gestation, and; and

producing an output that assesses the risk.

186. The method of claim 185, wherein the decision support system is a neural network.

187. The method of claim 185, wherein the decision-support system has been trained using a set of variables that do not include biochemical test data.

188. The method of claim 185, wherein the decision-support system has been trained using a set of variables that do not include the results of a test that detects fetal fibronectin in samples of mammalian body tissue and fluids.

189. The method of claim 185, wherein the set of variables further includes the result of a test that detects fetal fibronectin in cervico/vaginal samples.

190. In a computer system, a method for assessing the risk of delivery prior to completion of 35 weeks of gestation comprising:

(a) collecting observation values reflecting presence and absence of specified clinical data factors and storing the observed clinical data factors in storage means of the computer system, the specified clinical data factors comprising at least four up to all of the factors selected from the group consisting of;

Ethnic Origin Caucasian, Marital Status living with partner, EGA by sonogram, EGA at sampling, estimated date of delivery by best, cervical dilatation (CM), parity-preterm, vaginal bleeding at time of sampling, cervical consistency at time of sampling, and previous pregnancy without complication;

(b) applying the observation values from the memory means to a first decision-support system trained on samples of the specified factors; and

thereupon

(c) extracting from the first decision-support system an output value, wherein the output value is a quantitative objective aid to assess the risk of delivery prior to 35 weeks of gestation.

191. The method of claim 190, wherein the decision-support system comprises a neural network.

192. The method of claim 190, wherein at least five factors are selected.

193. The method of claim 190, wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in cervico/vaginal samples.

194. The method of claim 193, wherein the selected factors include the result of the test.

195. The method of claim 194, further comprising:

b1) applying said observation values from said memory means to a plurality of the first decision-support system, wherein each one of the first decision-support systems is trained on the samples of the specified factors with different starting weights for each training;

c1) extracting from the first decision-support system, output value pairs for each one of said first neural networks; and

d) forming a linear combination of said first ones of said output value pairs and forming a linear combination of said second ones of said output value pairs, to obtain a confidence index pair, said confidence index pair being said quantitative objective aid.

196. The method of claim 195, wherein the first decision support system is a neural network that comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having eleven input nodes, first, second and third second hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

197. The method of claim 195, wherein the first decision support system is a neural network and each of the plurality of first trained neural networks comprises a three-layer network comprising an input layer, a hidden layer and an output layer.

198. The method of claim 190, further comprising:

b1) applying said observation values from said memory means to a plurality of the first decision-support system, wherein each one of the first decision-support systems is trained on the samples of the specified factors with different starting weights for each training;

c1) extracting from the first decision-support system, output value pairs for each one of said first neural networks; and

d) forming a linear combination of said first ones of said output value pairs and forming a linear combination of said second ones of said output value pairs, to obtain a confidence index pair, said confidence index pair being said quantitative objective aid.

199. The method of claim 198, wherein the first decision support system is a neural network that comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having eleven input nodes, first, second and third second hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

200. The method of claim 198, wherein the first decision support system is a neural network and each of the plurality of first trained neural networks comprises a three-layer network comprising an input layer, a hidden layer and an output layer.

201. The method of claim 190, wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in mammalian body tissue and fluid samples.

202. A method for assessing the risk of delivery prior to completion of 35 weeks of gestation in a subject, comprising the steps of:

(a) collecting observation values reflecting presence and absence of specified factors and storing the observation factors in storage means of the computer system, the specified factors comprising;

Ethnic Origin Caucasian, Marital Status living with partner, EGA by sonogram, EGA at sampling, estimated date of delivery by best, cervical dilatation (CM), parity-preterm, vaginal bleeding at time of sampling, cervical consistency at time of sampling, and previous pregnancy without complication;

(b) performing a test on the subject to obtain results of a test that detects fetal fibronectin (fFN) in mammalian body tissue and fluid samples;

(c) entering the observation values and the fFN test results into a neural network trained on samples of the specified factors and the test results; and

thereupon

(d) producing from the trained neural network an output value pair, that is an indicator for the risk of delivery prior to 35 weeks of gestation.

203. The method of claim 202, further including the steps of:

(c1) applying the observation values and the relevant biochemical test results from the memory means to a plurality of the second neural networks, each one of the first neural networks being trained on the samples of the specified factors with starting weights for each training being randomly initialized;

(d1) extracting from each one of the first trained neural networks, output value pairs for each one of the first neural networks; and

(e) forming a linear combination of the first ones of the output value pairs and forming a linear combination of the second ones of the output value pairs, to obtain a confidence index pair, the confidence index pair being a final indicator for the risk of delivery prior to 35 weeks of gestation.

204. The method of claim 202, wherein the first trained neural network comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having eleven input nodes, first, second and third hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

205. The method of claim 202, wherein the sample is a cervico/vaginal samples.

206. A method for assessing the risk for delivery in seven or fewer days in a pregnant subject, comprising assessing a subset of variables containing at least three up to all of the following variables:

Ethnic Origin Caucasian;

Uterine contractions with or without pain;

Parity-abortions;

vaginal bleeding at time of sampling;

uterine contractions per hour; and

No previous pregnancies, by querying and testing the subject;

entering the results of the queries and tests into a computer system that comprises a decision-support system that has been trained to assess the risk of delivery within seven days; and

producing an output that assesses the risk of delivery within seven days.

207. The method of claim 206, wherein:

the variables further include the results of a test to detect fetal fibronectin (fFN) in a cervico/vaginal sample;

the selected variables include the results of the test; and

the method measures the risk of delivery in 7 days or few days from the time of obtaining the sample for the fFN.

208. The method of claim 207, wherein the decision support system is a neural network.

209. The method of claim 207, wherein the decision-support system has been trained using a set of variables that do not include biochemical test data.

210. The method of claim 207, wherein the decision-support system has been trained using a set of variables that do not include the results of a test that detects fetal fibronectin in cervico/vaginal samples.

211. The method of claim 206, wherein:

the variables further include the results of a test for to detect fetal fibronectin (fFN) in mammalian body tissue and fluid samples;

the selected variables include the results of the test; and

the method measures the risk of delivery in 7 days or few days from obtaining the sample for the fEN.

212. A method for assessing the risk for delivery in 7 days or fewer days, comprising:

(a) querying and examining test subjects to collect observation values reflecting presence and absence of specified clinical data factors and storing the observed clinical data factors in a storage medium of a computer system, the specified clinical data factors comprising at least four up to all of the factors selected from the group consisting of;

Ethnic Origin Caucasian, Uterine contractions with or without pain, Parity-abortions, vaginal bleeding at time of sampling, uterine contractions per hour, prior to, and number of previous pregnancies;

(b) applying the observation values from the storage medium to a first computer-based decision-support system trained on samples of the specified factors;

(c) producing from the first decision-support system an output value, wherein the output value is a quantitative objective aid to assess the risk of delivery in less than or in 7 days; and

(d) assessing the risk of delivery within seven days based upon the output value.

213. The method of claim 212, wherein the decision-support system comprises a neural network.

214. The method of claim 212, wherein at least five factors are selected.

215. The method of claim 212, wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in cervico/vaginal samples.

216. The method of claim 215, wherein the selected factors include the result of the test.

217. The method of claim 216, further comprising:

b1) applying said observation values from said memory means to a plurality of the first decision-support system, wherein each one of the first decision-support systems is trained on the samples of the specified factors with different starting weights for each training;

c1) extracting from the first decision-support system, output value pairs for each one of said first neural networks; and

d) forming a linear combination of said first ones of said output value pairs and forming a linear combination of said second ones of said output value pairs, to obtain a confidence index pair, said confidence index pair being said quantitative objective aid.

218. The method of claim 217, wherein the first decision support system is a neural network that comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having seven input nodes, first, second, third, forth and fifth second hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

219. The method of claim 217, wherein the first decision support system is a neural network and each of the plurality of first trained neural networks comprises a three-layer network comprising an input layer, a hidden layer and an output layer.

220. The method of claim 212, further comprising:

b1) applying said observation values from said memory means to a plurality of the first decision-support system, wherein each one of the first decision-support systems is trained on the samples of the specified factors with different starting weights for each training;

c1) extracting from the first decision-support system, output value pairs for each one of said first neural networks; and

d) forming a linear combination of said first ones of said output value pairs and forming a linear combination of said second ones of said output value pairs, to obtain a confidence index pair, said confidence index pair being said quantitative objective aid.

221. The method of claim 212, wherein the first decision support system is a neural network that comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having six input nodes, first, second, third, forth and fifth second hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

222. The method of claim 212, wherein the first decision support system is a neural network and each of the plurality of first trained neural networks comprises a three-layer network comprising an input layer, a hidden layer and an output layer.

223. The method of claim 212, wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in mammalian body tissue and fluid samples.

224. A method for assessing the risk for delivery in 7 days or fewer days in a subject, comprising the steps of:

(a) querying and examining the subject to collect observation values reflecting presence and absence of specified factors and storing the observation factors in storage means of the computer system, the specified factors comprising;

Ethnic Origin Caucasian, Uterine contractions with or without pain, Parity-abortions, vaginal bleeding at time of sampling, uterine contractions per hour, prior to and No previous pregnancies;

(b) performing and obtaining results from the patient of a test that detects fetal fibronectin (fFN) in mammalian body tissue and fluid samples, wherein the test is performed before, during or after the querying and examining step;

(c) applying the observation values and the fFN test results from the memory means to a neural network trained on samples of the specified factors and the test results; and

thereupon

(d) obtaining from the neural network an output value pair that is an indicator for the risk of delivery in 7 days or few days from obtaining the cervico/vaginal sample.

225. The method of claim 224, further including the steps of:

(c1) applying the observation values and the relevant biochemical test results from the memory means to a plurality of the second neural networks, each one of the first neural networks being trained on the samples of the specified factors with starting weights for each training being randomly initialized;

(d1) extracting from each one of the first trained neural networks, output value pairs for each one of the first neural networks; and

(e) forming a linear combination of the first ones of the output value pairs and forming a linear combination of the second ones of the output value pairs, to obtain a confidence index pair, the confidence index pair being the indicator of the risk for delivery in 7 days or fewer days.

226. The method of claim 224, wherein the first trained neural network comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having seven input nodes, first, second, third, fourth and fifth hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

227. The method of claim 224, wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in mammalian body tissue and fluid samples.

228. A method for assessing the risk for delivery in 14 or fewer days, comprising assessing a subset of variables containing at least three up to all of the following variables:

Ethnic Origin Hispanic;

Marital Status living with partner;

Uterine contractions with or without pain;

Cervical dilatation;

Uterine contractions per hour; and

No previous pregnancies, by querying and testing the subject; and

entering the results of the queries and tests into a computer system that comprises a decision-support system that has been trained to assess the risk of delivery within fourteen days; and

producing an output that assesses the risk for delivery in 14 or fewer days.

229. The method of claim 228, wherein:

the variables further include the results of a test for to detect fetal fibronectin (fFN) in a cervico/vaginal sample;

the selected variables include the results of the test; and

the method measures the risk of delivery in 14 days or few days from obtaining the sample for the fFN.

230. The method of claim 229, wherein the decision support system is a neural network.

231. The method of claim 229, wherein the decision-support system has been trained using a set of variables that do not include biochemical test data.

232. The method of claim 229, wherein the decision-support system has been trained using a set of variables that do not include the results of a test that detects fetal fibronectin in cervico/vaginal samples.

233. The method of claim 228, wherein:

the variables further include the results of a test that detects fetal fibronectin in mammalian body tissue and fluid samples;

the selected variables include the results of the test; and

the method measures the risk of delivery in 14 days or few days from obtaining the sample for the fFN.

234. A method for assessing the risk for delivery in 14 days or fewer days in a subject, comprising:

(a) querying and examining the subject to collect observation values reflecting presence and absence of specified clinical data factors and storing the observed clinical data factors in storage means of the computer system, the specified clinical data factors comprising at least four up to all of the factors selected from the group consisting of;

Ethnic Origin Hispanic, Marital Status living with partner, Uterine contractions with or without pain, cervical dilatation, Uterine contractions per hour, and No previous pregnancies;

(b) applying the observation values from the memory means to a first computer-based decision-support system trained on samples of the specified factors; and

thereupon

(c) producing from the decision-support system an output value that is a quantitative objective aid to assess the risk of delivery in less than or in 14 days.

235. The method of claim 234, wherein the decision-support system comprises a neural network.

236. The method of claim 234, wherein at least five factors are selected.

237. The method of claim 234, wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in cervico/vaginal samples.

238. The method of claim 234, wherein the selected factors include the result of the test.

239. The method of claim 238, further comprising:

b1) applying said observation values from said memory means to a plurality of the first decision-support system, wherein each one of the first decision-support systems is trained on the samples of the specified factors with different starting weights for each training;

c1) extracting from the first decision-support system, output value pairs for each one of said first neural networks; and

d) forming a linear combination of said first ones of said output value pairs and forming a linear combination of said second ones of said output value pairs, to obtain a confidence index pair, said confidence index pair being said quantitative objective aid.

240. The method of claim 238, wherein the first decision support system is a neural network that comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having seven input nodes, first, second, third, forth and fifth second hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

241. The method of claim 238, where in the first decision support system is a neural network and each of the plurality of first trained neural networks comprises a three-layer network comprising an input layer, a hidden layer and an output layer.

242. The method of claim 234, further comprising:

b1) applying said observation values from said memory means to a plurality of the first decision-support system, wherein each one of the first decision-support systems is trained on the samples of the specified factors with different starting weights for each training;

c1) extracting from the first decision-support system, output value pairs for each one of said first neural networks; and

d) forming a linear combination of said first ones of said output value pairs and forming a linear combination of said second ones of said output value pairs, to obtain a confidence index pair, said confidence index pair being said quantitative objective aid.

243. The method of claim 234, wherein the first decision support system is a neural network that comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having six input nodes, first, second, third, forth and fifth second hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

244. The method of claim 234, wherein the first decision support system is a neural network and each of the plurality of first trained neural networks comprises a three-layer network comprising an input layer, a hidden layer and an output layer.

245. The method of claim 234, wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in mammalian body tissue and fluid samples.

246. A method for assessing the risk for delivery in 14 days or fewer days in a patient, comprising the steps of:

(a) querying and examining the patient to collect observation values reflecting presence and absence of specified factors and storing the observation factors in a storage medium of a computer system, the specified factors comprising;

Ethnic Origin Hispanic, Marital Status living with partner, Uterine contractions with or without pain, cervical dilatation, Uterine contractions per hour, and No previous pregnancies;

(b) performing and obtaining results from the patient of a test that detects fetal fibronectin (fFN) in mammalian body tissue and fluid samples, wherein the test is performed prior to, during or after step (a);

(c) applying the observation values and the fFN test results from the memory means to a neural network trained on samples of the specified factors and the test results; and

(d) extracting from the neural network an output value pair that is a preliminary indicator for the risk of delivery in 14 days or few days from obtaining the body tissue of fluid sample.

247. The method of claim 246, further including the steps of:

(c1) applying the observation values and the relevant biochemical test results from the memory means to a plurality of the second neural networks, each one of the first neural networks being trained on the samples of the specified factors with starting weights for each training being randomly initialized;

(d1) extracting from each one of the first trained neural networks, output value pairs for each one of the first neural networks; and

(e) forming a linear combination of the first ones of the output value pairs and forming a linear combination of the second ones of the output value pairs, to obtain a confidence index pair, the confidence index pair being the indicator of the risk for delivery in 14 days or fewer days.

248. The method of claim 246, wherein the first trained neural network comprises a three-layer network containing an input layer, a hidden layer and an output layer, the input layer having seven input nodes, first, second, third, fourth and fifth hidden layer nodes, a hidden layer bias for each hidden layer node, first and second output layer nodes in the output layer, and an output layer bias for each output layer node.

249. The method of claim 246, wherein the sample is a cervico/vaginal sample.

250. A computer system, comprising a neural network or plurality thereof trained for assessing the risk of delivery within a selected time period, wherein the time period is within seven or fourteen days of performing a biochemical test to measure fetal fibronectin in a sample from a pregnant subject or prior to thirty five weeks of gestation.

251. A computer system, comprising a neural network or plurality thereof trained for diagnosing endometriosis.