Methods for selecting, developing and improving diagnostic tests for pregnancy-related conditions

• US 6,556,977 B1
• Filed: 08/14/1998
• Issued: 04/29/2003
• Est. Priority Date: 08/14/1997
• Status: Expired due to Term

• Current Assignee: Cytyc Corporation (Hologic Incorporated)
• Original Assignee: Cytyc Prenatal Products Corp. (Hologic Incorporated)
• Inventors: DeSieno, Duane D., Lapointe, Jerome
• Primary Examiner(s): STARKS, WILBERT L
• Application Number:
  US09/134,636
• Time in Patent Office: 1,719 Days
• Field of Search: 706/15, 706/23, 706/45
• 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 of assessment of the risk of preterm delivery or the risk of delivery within a selected period of time, comprising:

(a) obtaining a set of candidate variables based upon data obtained by making querying and examining patients at risk for preterm delivery, designating the candidate variables as a first set of candidate variables and entering them into a computer memory or computer-readable storage medium;

(b) selecting a set of important selected variables by;

(i) 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;

(ii) 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;

(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, when the best candidate variable does not improve performance, the process is completed, thereby producing a set of selected variables

(c) training a decision-support system using the selected final set of important selected variables to produce a test for assessment of the risk of preterm delivery or risk of delivery within a selected time period, wherein assessment of delivery within a selected period of time refers either to prediction of delivery at a particular gestational age, or the risk of delivery within a given time interval

(d) storing the test for assessment of the risk in a computer memory or on a computer-readable medium.

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

3. The method of claim 1, wherein the method of diagnosis assesses the likelihood of preterm delivery.

4. The method of claim 1, wherein the candidate variables include biochemical test data.

5. A computer readable storage medium produced by the method of claim 1.

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

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

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

9. A method of improving the effectiveness of a diagnostic biochemical test for preterm delivery or for the risk of delivery within a selected period of time, comprising:

(a) selecting a set of important selected variables by the method of claim 1;

(b) 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

(c) training a decision-support system using the selected final set of important selected variables and the biochemical test data to produce a test that is more effective in assessing the risk or preterm delivery or delivery within a selected time period than the biochemical test alone.

(d) storing the test for assessment of the risk in a computer memory or on a computer-readable medium.

10. The method of claim 9, wherein the candidate variables are responses to queries 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;

marital status single;

marital status married;

marital status divorced or separated;

marital status widowed;

marital status living with partner;

marital 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 Contractions 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 hrs 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 pregnacy with a complication not listed above.

11. The method of claim 10, wherein the biochemical test is a test that detects fetal fibronectin in cervico/vaginal samples;

determines the level of a local inflammatory product protein in cervico/vaginal samples;

or estriol or estretol in saliva.

12. The method of claim 9, wherein the candidate variables are responses to queries selected from the group consisting of:

Caucasian;

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.

13. The method of claim 9, wherein the candidate variables are responses to queries selected from the group consisting of:

Caucasion;

Uterine contractions with or without pain;

Parity-abortions;

Vaginal bleeding at time of sampling;

Uterine contractions per hour; and

No previous pregnancies.

14. A computer-based diagnostic test produced by the method of claim 4.

15. 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 responses to the following queries:

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; 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 the risk of delivery prior to 35 weeks of gestation, and thereby assessing the risk.

16. The method of claim 15, wherein the decision support system is a neural network.

18. The method of claim 15, 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.

19. The method of claim 18, 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.

20. The method of claim 19, 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.

21. The method of claim 15, wherein the set of variables further includes the result of a test that detects fetal fibronectin in cervico/vaginal samples.

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

22. A method for assessing the risk of delivery prior to completion of 35 weeks of gestation 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 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 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 assess the risk of delivery prior to 35 weeks of gestation.

23. The method of claim 22, wherein the decision-support system comprises a neural network.

24. The method of claim 22, wherein at least five factors are selected.

25. The method of claim 22, wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in cervico/vaginal samples;

the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples;

or the result of a test that assesses estriol or estretol in saliva.

26. The method of claim 25, wherein the selected factors include the result of the test.

27. The method of claim 26, 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.

28. The method of claim 27, 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.

29. The method of claim 27, 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.

30. The method of claim 22, 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.

31. The method of claim 30, 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.

32. The method of claim 30, 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.

33. The method of claim 22, wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in mammalian body tissue and fluid samples.

38. A computer-based diagnostic test produced by the method of claim 24.

39. A computer readable medium or computer memory, comprising a decision-support system produced by the method of claim 24.

40. A computer-readable medium produced by the method of claim 24.

34. In a computer system, a method for assessing the risk of delivery in a subject prior to completion of 35 weeks of gestation, 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, 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) testing a patient and obtaining results of the test from the patient, wherein the test detects fetal fibronectin (fFN) in mammalian body tissue and fluid samples, and/or the test determines the level of a local inflammatory product protein in cervico/vaginal samples; and

/or of a test that assesses estriol or estretol in saliva, 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

thereupon

(d) extracting from the trained neural network an output value pair, the output value pair being a preliminary indicator for the risk of delivery prior to 35 weeks of gestation.

35. The method of claim 34, 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.

36. The method of claim 34, 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.

37. The method of claim 34, wherein the sample is a cervico/vaginal sample.

41. A method for assessing the risk for delivery in 7 or fewer days, 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; 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 the risk of delivery within seven days, and thereby assessing the risk.

42. The method of claim 41, wherein:

the variables further include the result of a test for to detect fetal fibronectin (fFN) in a cervico/vaginal sample and/or the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples and/or the result of a test that assesses estriol or estretol in saliva;

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 fFN.

43. The method of claim 42, wherein the decision support system is a neural network.

44. The method of claim 42, wherein the decision-support system has been trained using a set of variables that do not include biochemical test data.

45. The method of claim 41, wherein:

the variables further include the result of a test for to detect fetal fibronectin (fFN) in mammalian body tissue and fluid samples and/or the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples and/or the result of a test that assesses estriol or estretol in saliva;

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 fFN.

46. A method for assessing in a subject the risk for delivery in 7 days or fewer days, 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 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 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 assess the risk of delivery prior to 35 weeks of gestation.

47. The method of claim 46, wherein the decision-support system comprises a neural network.

48. The method of claim 46, wherein at least five factors are selected.

49. The method of claim 46, wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in cervico/vaginal sample and/or the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples and/or the result of a test that assesses estriol or estretol in saliva.

50. The method of claim 49, wherein the selected factors include the result of the test.

51. The method of claim 50, 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.

52. The method of claim 51, 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.

53. The method of claim 51, 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.

54. The method of claim 46, 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.

55. The method of claim 46, 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.

56. The method of claim 46, 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.

57. The method of claim 46, wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in mammalian body tissue and fluid samples.

58. In a computer system, a method for assessing the risk for delivery in 7 days or fewer days, 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) testing a patient and obtaining results of the test from the patient, wherein the test detects fetal fibronectin (fFN) in mammalian body tissue and fluid samples, and/or the test determines the level of a local inflammatory product protein in cervico/vaginal samples; and

/or of a test that assesses estriol or estretol in saliva, 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 second neural network trained on samples of the specified factors and the test results; and

thereupon

(d) extracting from the second trained neural network an output value pair, the output value pair being a preliminary indicator for the risk of delivery in 7 days or few days from obtaining the cervico/vaginal sample.

59. The method of claim 58, 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.

60. The method of claim 58, 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.

61. The method of claim 58, wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in mammalian body tissue and fluid samples and/or the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples and/or the result of a test that assesses estriol or estretol in saliva.

62. 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 the risk of delivery within fourteen days, and thereby assessing the risk.

63. The method of claim 62, wherein:

the variables further include the result of a test for to detect fetal fibronectin (fFN) in a cervico/vaginal sample and/or the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples and/or the result of a test that assesses estriol or estretol in saliva;

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.

64. The method of claim 63, wherein the decision support system is a neural network.

65. The method of claim 63, wherein the decision-support system has been trained using a set of variables that do not include biochemical test data.

66. The method of claim 63, 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.

67. The method of claim 62, wherein:

comprise the result of a test that detects fetal fibronectin in mammalian body tissue and fluid samples and/or the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples and/or the result of a test that assesses estriol or estretol in saliva 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.

68. A method for assessing in a subject the risk for delivery in 14 days or fewer days, 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 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 in less than or in 14 days.

69. The method of claim 68, wherein the decision-support system comprises a neural network.

70. The method of claim 68, wherein at least five factors are selected.

71. The method of claim 68, wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in cervico/vaginal samples.

72. The method of claim 68, wherein the selected factors include the result of the test.

73. The method of claim 72, 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.

74. The method of claim 72, 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.

75. The method of claim 72, 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.

76. The method of claim 68, 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.

77. The method of claim 68, 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.

78. The method of claim 68, 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.

79. The method of claim 68, wherein the clinical factors further comprise the result of a test that detects fetal fibronectin in mammalian body tissue and fluid samples and/or the result of a test that determines the level of a local inflammatory product protein in cervico/vaginal samples and/or the result of a test that assesses estriol or estretol in saliva.

80. In a computer system, a method for assessing the risk for delivery in 14 days or fewer days, 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 Hispanic, Marital Status living with partner, Uterine contractions with or without pain, cervical dilatation, Uterine contractions per hour, and No previous pregnancies;

(b) testing a patient and obtaining results of the test from the patient, wherein the test detects fetal fibronectin (fFN) in mammalian body tissue and fluid samples, and/or the test determines the level of a local inflammatory product protein in cervico/vaginal samples; and

/or of a test that assesses estriol or estretol in saliva, 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 second neural network trained on samples of the specified factors and the test results; and

thereupon

(d) extracting from the second trained neural network an output value pair, the output value pair being a preliminary indicator for the risk of delivery in 14 days or few days from obtaining the cervico/vaginal sample.

81. The method of claim 80, wherein the sample is a cervico/vaginal sample and the test detects fetal fibronectin.

82. A computer system, comprising a neural network or plurality thereof trained for assessing the risk of preterm delivery or imminent delivery within in a predetermined time frame.

83. The system of claim 82, wherein the time frame is 7 days.

84. The system of claim 82, wherein the time frame is 14 days.

85. The system of claim 82, wherein the risk of delivery before 35 weeks is assessed.