Method for predicting the onset or change of a medical condition

• US 20050119534A1
• Filed: 10/19/2004
• Published: 06/02/2005
• Est. Priority Date: 10/23/2003
• Status: Abandoned Application

• Current Assignee: Pfizer Inc., Pfizer Products Incorporated (Pfizer Inc.)
• Original Assignee: Pfizer Inc.
• Inventors: Ostroff, Jack, Freston, James W., Trost, Donald Craig
• Application Number:
  US10/968,675
• Publication Number:
  US 20050119534A1
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Nonlinear generalized dynamic regression analysis system and method of the present invention preferably uses all available data at all time points and their measured time relationship to each other to predict responses of a single output variable or multiple output variables simultaneously. The present invention, in one aspect, is a system and method for predicting whether an intervention administered to a patient changes the physiological, pharmacological, pathophysiological, or pathopsychological state of the patient with respect to a specific medical condition. The present invention uses the theory of martingales to derive the probabilistic properties for statistical evaluations. The approach uniquely models information in the following domains: (1) analysis of clinical trials and medical records including efficacy, safety, and diagnostic patterns in humans and animals, (2) analysis and prediction of medical treatment cost-effectiveness, (3) the analysis of financial data, (4) the prediction of protein structure, (5) analysis of time dependent physiological, psychological, and pharmacological data, and any other field where ensembles of sampled stochastic processes or their generalizations are accessible. A quantitative medical condition evaluation or medical score provides a statistical determination of the existence or onset of a medical condition.

Claims

1. A method for predicting whether a subject has a heightened risk of the onset of a specific medical condition, the method comprising the steps of:

a. defining an n-dimensional space corresponding to a respective n-number of clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria useful for diagnosing the medical condition wherein points disposed within a first portion of the n-dimensional space signify the absence of a clinician-cognizable indication of the specific medical condition, and points disposed within a second portion of the n-dimensional space signify the presence of a clinician-cognizable indication of the medical condition;

b. obtaining subject data corresponding to the respective clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria for the subject;

c. calculating vectors based on incremental time-dependent changes in the respective subject data, the vectors disposed within the first portion of the n-dimensional space signifying the absence of a clinician-cognizable indication of the specific medical condition; and

d. determining whether the vectors comprise a clinician-cognizable vector pattern, which signifies that the subject, while having no clinician-cognizable indication of the specific medical condition, nonetheless has a heightened risk of the onset of the medical condition.

2. The method of claim 1, wherein the clinician-cognizable vector pattern comprises a divergent vector.

3. The method of claim 1, wherein the clinician-cognizable vector pattern is an indication of an adverse event or adverse therapeutic result for the subject.

4. The method of claim 1, wherein the vector analysis is performed from the subject data using a non-parametric, non-linear, generalized dynamic regression analysis system.

5. The method of claim 4, wherein the non-parametric, non-linear, generalized dynamic regression analysis system is a model for an underlying population of stochastic processes represented by an ensemble of sample paths of the first and second, or subsequent, time period vectors.

6. The method of claim 5, wherein the non-parametric, non-linear, generalized dynamic regression analysis system uses the general equation:

dY(t)=X(t)dB(t)+dM(t)

wherein Y(t) or dY(t) is the stochastic differential of a right-continuous sub-martingale, X(t) is an n×

p matrix of clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria, dB(t) is a p-dimensional vector of unknown regression functions, and dM(t) is a stochastic differential n-vector of local square-integrable martingales.

7. The method of claim 6, wherein the respective clinician cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria are external covariates.

8. The method of claim 6, wherein the respective clinician cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria are functions of previous outcomes of Y.

9. The method of claim 8, wherein the functions of previous outcomes of Y are auto-regressions.

10. The method of claim 6, wherein B(t) is an unknown parameter estimated by any acceptable statistical estimation procedure.

11. The method of claim 10, wherein the acceptable statistical estimation procedure is selected from the group consisting of:

the Generalized Nelson-Aalen Estimator, Baysesian estimation, the Ordinary Least Squares Estimator, the Weighted Least Squares Estimator, and the Maximum Likelihood Estimator.

12. The method of claim 1, wherein the first portion comprises a content that comprises a boundary, and the clinician-cognizable vector pattern comprises a divergent vector comprising a direction and magnitude so as to extend from within the content towards the boundary signifying the heightened risk of the onset of the specific medical condition.

13. The method of claim 1, wherein the vectors disposed in the first portion exhibit a stochastic noise process.

14. The method of claim 13, wherein the stochastic noise process is Brownian motion.

15. The method of claim 14, wherein the Brownian motion is constrained.

16. The method of claim 1, further comprising the step of administering an intervention to the subject, wherein the intervention is suspected to have a clinician-cognizable propensity to effect the heightened risk of the onset of the specific medical condition.

17. The method of claim 16, wherein the specific medical condition is an adverse medical condition or side effect.

18. The method of claim 1, further comprising the step of administering an intervention to the subject, wherein the intervention is suspected to have a clinician-cognizable propensity to increase or decrease the heightened risk of the onset of the specific medical condition.

19. The method of claim 18, wherein the intervention comprises administering a drug to the subject, and wherein the drug has a clinician cognizable propensity to increase the risk of the specific medical condition, and said specific medical condition comprises an adverse medical condition or side effect.

20. The method of claim 1, wherein the method is computer-based.

102. The database of claim 9.5, the database vector patterns comprising a visual format.

137. The method of claim 12-7, wherein step (f) further comprises displaying graphs of the medical score for the individual at specific times in time-consecutive order as a moving image showing the change in the medical score for the individual over time.

21. A method for predicting whether a subject having a specific medical condition has a heightened propensity of the onset of a diminution in the specific medical condition, the method comprising the steps of:

a. defining an n-dimensional space corresponding to a respective n-number of clinician-cognizable physiological, pharmacological, pathophysiological or pathopsychological criteria useful for diagnosing the specific medical condition,

wherein points disposed within a first portion of the n-dimensional space signify the presence of a clinician-cognizable indication of the specific medical condition, and points disposed within a second portion of the n-dimensional space signify the absence of a clinician-cognizable indication of the specific medical condition;

b. obtaining subject data corresponding to the respective clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria for the subject;

c. calculating vectors based on incremental time-dependent changes in the respective subject data, the vectors disposed within the first portion of the n-dimensional space signifying that the subject has the specific medical condition; and

d. determining whether the vectors further comprise a clinician-cognizable vector pattern, which signifies that the subject, while having the specific medical condition, nonetheless has a heightened propensity of the onset of a diminution in the medical condition.

22. The method of claim 21, wherein the clinician-cognizable vector pattern comprises a divergent vector.

23. The method of claim 21, wherein the clinician-cognizable vector pattern is an indication of a positive result of a therapeutic intervention for the subject.

24. The method of claim 21, wherein step (c) comprises vector analysis performed from the subject data using a non-parametric, non-linear, generalized dynamic regression analysis system.

25. The method of claim 24, wherein the non-parametric, non-linear, generalized dynamic regression analysis system is a model for an underlying population of stochastic processes represented by an ensemble of sample paths of the first and second time period vectors.

26. The method of claim 25, wherein the non-parametric, non-linear, generalized dynamic regression analysis system uses the general equation:

dY(t)=X(t)dB(t)+dM(t)

wherein Y(t) or dY(t) is the stochastic differential of a right-continuous sub-martingale, X(t) is an n×

p matrix of clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria, dB(t) is a p-dimensional vector of unknown regression functions, and dM(t) is a stochastic differential n-vector of local square-integrable martingales.

27. The method of claim 26, wherein the respective clinician cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria are external covariates.

28. The method of claim 26, wherein the respective clinician cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria are functions of previous outcomes of Y.

29. The method of claim 28, wherein the functions of previous outcomes of Y are auto-regressions.

30. The method of claim 26, wherein B(t) is an unknown parameter estimated by any acceptable statistical estimation procedure.

31. The method of claim 30, wherein the acceptable statistical estimation procedure is selected from the group consisting of:

the Generalized Nelson-Aalen Estimator, Bayesian estimation, the Ordinary Least Squares Estimator, the Weighted Least Squares Estimator, and the Maximum Likelihood Estimator.

32. The method of claim 21, wherein the first portion comprises a content that comprises a boundary, and the clinician-cognizable vector pattern comprises a divergent vector comprising a direction and magnitude so as to extend towards the boundary signifying the heightened risk of the onset of the specific medical condition.

33. The method of claim 21, wherein the vectors disposed in the first portion exhibit a stochastic noise process.

34. The method of claim 33, wherein the stochastic noise process is Brownian motion.

35. The method of claim 34, wherein the Brownian motion is constrained.

36. The method of claim 23, further comprising administering a therapeutic intervention to the subject.

37. The method of claim 36, wherein the therapeutic intervention is suspected to have a clinician-cognizable propensity to diminish the specific medical condition.

38. The method of claim 36, wherein the intervention is suspected to have a clinician-cognizable propensity to treat the specific medical condition.

39. The method of claim 21, wherein the specific medical condition is an adverse medical condition or side effect.

40. The method of claim 21, wherein the method is computer-based.

41. A method for predicting whether an intervention administered to a patient changes the physiological, pharmacological, pathophysiological, or pathopsychological state of the patient with respect to a specific medical condition, the method comprises the steps of:

a. defining a space corresponding to respective clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria useful for diagnosing the specific medical condition;

b. defining a content in the space wherein points disposed within the content signify the absence of a clinician-cognizable indication of the specific medical condition, and points disposed outside the content signify the presence of a clinician-cognizable indication of the specific medical condition;

c. obtaining patient data corresponding to the respective clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria for the patient in;

(i) a first condition corresponding to a first time period before the intervention is administered to the patient, and

(ii) a second condition corresponding to a second time period after the intervention is administered to the patient;

d. calculating first condition vectors disposed within the content for the first condition and second condition vectors disposed within the content for the second condition, the first and second condition vectors being based on incremental time-dependent changes in the respective patient data from the first and second conditions; and

e. determining whether the second condition vectors further comprise a clinician-cognizable vector pattern, which signifies that while the patient, by virtue of the first and second condition vectors being disposed within the content, has no clinician-cognizable indication of the specific medical condition, nonetheless has a heightened risk of the onset of the specific medical condition after the intervention is administered.

42. The method of claim 41, wherein the intervention comprises a drug administered to the patient.

43. The method of claim 41, wherein the intervention comprises a placebo administered to the patient.

44. The method of claim 41, wherein the step (e) comprises plotting the first and second condition vectors in the space.

45. The method of claim 41, wherein step (h) further comprises the step of determining the absence of the clinician-cognizable vector pattern from the second condition vectors, which absence signifies that the patient does not have a heightened risk of the onset of the specific medical condition after the intervention is administered.

46. The method of claim 41, wherein the content comprises an n-dimensional manifold or n-dimensional sub-manifold.

47. The method of claim 41, wherein the content comprises an n-dimensional hyperellipsoid.

48. The method of claim 41, wherein the clinician-cognizable vector pattern comprises a divergent vector.

49. A method for predicting whether an intervention suspected of effecting a specific adverse medical condition or side effect when administered to a patient changes the physiological, pharmacological, pathophysiological, or pathopsychological state of a patient with respect to the specific adverse medical condition or side effect, the method comprises the steps of:

a. defining a space comprising n-axes intersecting at a point p, the n-axes corresponding to respective clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria useful for diagnosing the specific medical condition or side effect;

b. defining a content in the space based on;

(i) first physiological, pharmacological, pathophysiological, or pathopsychological data obtained from a statistically significant sample of people with no clinician-cognizable indication of the specific adverse medical condition or side effect, and

(ii) second physiological, pharmacological, pathophysiological, or pathopsychological data obtained from a statistically significant sample of people with a clinician-cognizable indication of the specific adverse medical condition or side effect,

wherein points disposed within the content signify the absence of a clinician-cognizable indication of the specific adverse medical condition or side effect, and points disposed outside the content signify the presence of a clinician-cognizable indication of the specific adverse medical condition or side effect;

c. obtaining patient data corresponding to the respective clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria for the specific patient in;

(i) a first condition corresponding to a first time period before the intervention is administered to the specific patient, and

(ii) a second condition corresponding to a second time period after the intervention is administered to the specific patient;

d. calculating first condition vectors for the first condition and second condition vectors for the second condition, the first and second condition vectors being based on incremental time-dependent changes in the respective specific patient data from the first and second conditions;

e. evaluating the first and second condition vectors with respect to the space;

f. determining whether the first condition vectors are lacking a clinician-cognizable vector pattern, which signifies that the patient has no clinician-cognizable indication of the specific adverse medical condition or side effect during the first time period before the intervention is administered; and

g. determining whether the second condition vectors are lacking a clinician-cognizable vector pattern, which signifies that the patient has no clinician-cognizable heightened risk of the onset of the specific adverse medical condition side effect during the second time period after the intervention is administered.

50. The method of claim 49, wherein the specific adverse medical condition or side effect is hepatotoxicity.

51. The method of claim 50, wherein the criteria comprise a plurality of LFTs.

52. The method of claim 51, wherein the LFTs are selected from the group consisting of ALT, ALP, AST, GGT, and combinations thereof.

53. The method of claim 49, further comprising the step of

h. determining whether the second condition vectors comprise a clinician-cognizable vector pattern, which signifies that the patient, while having no clinician-cognizable indication of the specific adverse medical condition or side effect, nonetheless has a heightened risk of the onset of the specific medical condition or side effect.

54. The method of claim 53, wherein the side effect is hepatotoxicity.

55. The method of claim 54, wherein the criteria comprise a plurality of LFTs.

56. The method of claim 55, wherein the LFTs are selected from the group consisting of:

ALT, ALP, AST, GGT, and combinations thereof.

57. A method for predicting whether an intervention administered to a patient changes the physiological, pharmacological, pathophysiological, or pathopsychological state of the patient with respect to a specific medical condition, the method comprises the steps of:

a. defining a space corresponding to respective clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria useful for diagnosing the specific medical condition;

b. defining a content in the space wherein points disposed within the content signify the presence of a clinician-cognizable indication of the specific medical condition, and points disposed outside the content signify the absence of a clinician-cognizable indication of the specific medical condition;

c. obtaining patient data corresponding to the respective clinician-cognizable pathophysiological, pharmacological, pathophysiological, or pathopsychological criteria for the patient in;

(i) a first condition corresponding to a first time period before the intervention is administered to the patient, and

(ii) a second condition corresponding to a second time period after the intervention is administered to the patient;

d. calculating first condition vectors within the content for the first condition and second condition vectors within the content for the second condition, the first and second condition vectors being based on incremental time-dependent changes in the respective patient data from the first and second conditions; and

e. determining whether the second condition vectors comprise a clinician-cognizable vector pattern, which signifies that while the patient, by virtue of the first and second condition vectors being disposed within the content, has the specific medical condition, nonetheless has a heightened propensity of the onset of the diminution of the specific medical condition after the intervention is administered.

58. The method of claim 57, wherein the intervention comprises a drug administered to the patient.

59. The method of claim 57, wherein the intervention comprises a placebo administered to the patient.

60. The method of claim 57, wherein the step (e) comprises plotting the first and second condition vectors in the space.

61. The method of claim 57, wherein step(h) further comprises the step of determining the absence of the clinician-cognizable vector pattern from the second condition vectors, which absence signifies that the patient does not have a heightened propensity of the onset of the diminution of the specific medical condition after the intervention is administered.

62. The method of claim 57, wherein the content comprises an n-dimensional manifold or n-dimensional sub-manifold.

63. The method of claim 57, wherein the content comprises an n-dimensional hyperellipsoid.

64. The method of claim 57, wherein the clinician-cognizable vector pattern comprises a divergent vector.

65. A method for predicting whether an intervention suspected of effecting a diminution of a specific adverse medical condition or side effect when administered to a patient changes the clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological state of a patient with respect to the specific adverse medical condition or side effect, the method comprises the steps of:

a. defining a space comprising n-axes intersecting at a point p, the n-axes corresponding to respective clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria useful for diagnosing the specific medical condition or side effect;

b. defining a content in the space based on;

(i) first physiological, pharmacological, pathophysiological, or pathopsychological data obtained from a statistically significant sample of people with no clinician-cognizable indication of the specific medical condition or side effect, and

(ii) second physiological, pharmacological, pathophysiological, or pathopsychological data obtained from a statistically significant sample of people with a clinician-cognizable indication of the specific medical condition or side effect,

wherein points disposed within the content signify the presence of a clinician-cognizable indication of the specific adverse medical condition or side effect, and points disposed outside the content signify the absence of a clinician-cognizable indication of the specific adverse medical condition or side effect;

c. obtaining patient data corresponding to the respective clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria for the specific patient in;

(i) a first condition corresponding to a first time period before the intervention is administered to the patient, and

(ii) a second condition corresponding to a second time period after the intervention is administered to the patient;

d. calculating first condition vectors for the first condition and second condition vectors for the second condition, the first and second condition vectors being based on incremental time-dependent changes in the respective specific patient data from the first and second conditions;

e. evaluating the first and second condition vectors with respect to the space;

f. determining whether the first condition vectors disposed within the content and are lacking a clinician-cognizable vector pattern, which signifies that the patient has a clinician-cognizable indication of the specific adverse medical condition or side effect during the first time period before the intervention is administered; and

g. determining whether the second condition vectors are disposed within the content and are lacking a clinician-cognizable vector pattern, which signifies that the patient has a clinician-cognizable indication of the specific adverse medical condition or side effect during the second time period after the intervention is administered.

66. The method of claim 65, wherein the side effect is hepatotoxicity.

67. The method of claim 66, wherein the criteria comprise a plurality of LFTs.

68. The method of claim 67;

wherein the LFTs are selected from the group consisting of;

ALT, ALP, AST, GGT, and combinations thereof.

69. The method of claim 65, further comprising the step of:

h. determining whether the second condition vectors are disposed within the content and comprise a clinician-cognizable vector pattern, which signifies that the specific patient, while having the clinician-cognizable indication of the specific adverse medical condition or side effect, nonetheless has a heightened propensity of the diminution of the specific adverse medical condition or side effect.

70. The method of claim 69, wherein the side effect is hepatotoxicity.

71. The method of claim 70, wherein the criteria comprise a plurality of LFTs.

72. The method of claim 71, wherein the LFTs are selected from the group consisting of:

ALT, ALP, AST, GGT, and combinations thereof.

73. A method for minimizing medical costs by predicting whether an intervention administered to a patient will likely adversely change the physiological, physiological, pharmacological, pathophysiological, or pathopsychological state of the patient with respect to a specific medical condition, the method comprises the steps of:

a. defining a space comprising n-axes intersecting at a point p, the n-axes corresponding to respective clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria useful for diagnosing the specific medical condition;

b. defining a content in the space based on;

(i) first physiological, pharmacological, pathophysiological, or pathopsychological data obtained from a statistically significant sample of people with no clinician-cognizable indication of the specific medical condition, and

(ii) second physiological, pharmacological, pathophysiological, or pathopsychological data obtained from a statistically significant sample of people with a clinician-cognizable indication of the specific medical condition,

wherein points disposed within the content signify the absence of a clinician-cognizable indication of the specific medical condition, and points disposed outside the content signify the presence of a clinician-cognizable indication of the specific medical condition;

c. obtaining patient data corresponding to the respective clinician-cognizable physiological, physiological, pharmacological, pathophysiological, or pathopsychological criteria for the patient in;

(i) a first condition corresponding to a first time period before the intervention is administered to the patient, and

(ii) a second condition corresponding to a second time period after the intervention is administered to the patient;

d. calculating first condition vectors for the first condition and second condition vectors for the second condition, the first and second condition vectors being based on incremental time-dependent changes in the respective patient data in the respective first and second conditions;

e. evaluating the first and second condition vectors with respect to the space;

f. determining whether the first condition vectors are disposed within the content and are lacking a clinician-cognizable vector pattern, which signifies that the patient has no clinician-cognizable indication of the specific medical condition during the first time period before the intervention is administered; and

g. determining whether the second condition vectors are disposed within the content and comprise a clinician-cognizable vector pattern, which signifies that the patient, while having no clinician-cognizable indication of the specific medical condition, nonetheless has a heightened risk of the onset of the specific medical condition,

whereby the patient while not having the specific medical condition is advised of the heightened risk of the specific medical condition by the administration of the intervention and the further administration of the intervention is evaluated and diminished or discontinued to minimize liability that might result from the continued administration of the intervention.

74. The method of claim 73, wherein the intervention comprises a drug administered to the patient.

75. The method of claim 73, further comprising (i) discontinuing administration of the intervention to the patient.

76. A method for minimizing liability by predicting whether an intervention administered to a patient will likely adversely change the physiological, pharmacological, pathophysiological, or pathopsychological state of the patient with respect to a specific medical condition, the method comprises the steps of:

a. defining a space comprising n-axes intersecting at a point p, the n-axes corresponding to respective clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria useful for diagnosing the specific medical condition;

b. defining a content in the space based on;

(i) first physiological, pharmacological, pathophysiological, or pathopsychological data obtained from a statistically significant sample of people with no clinician-cognizable indication of the specific medical condition, and

(ii) second physiological, pharmacological, pathophysiological, or pathopsychological data obtained from a statistically significant sample of people with a clinician-cognizable indication of the specific medical condition,

wherein points disposed within the content signify the absence of a clinician-cognizable indication of the specific medical condition, and points disposed outside the content signify the presence of a clinician-cognizable indication of the specific medical condition;

c. obtaining patient data corresponding to the respective clinician-cognizable physiological, pharmacological, pathophysiological or pathopsychological criteria for the patient in;

(i) a first condition corresponding to a first time period before the intervention is administered to the patient, and

(ii) a second condition corresponding to a second time period after the intervention is administered to the patient;

d. calculating first condition vectors for the first condition and second condition vectors for the second condition, the first and second condition vectors being based on incremental time-dependent changes in the respective patient data in the respective first and second conditions;

e. evaluating the first and second condition vectors with respect to the space;

f. determining whether the first condition vectors are disposed within the content and comprise a sub-content having no clinician-cognizable vector pattern, which signifies that the patient has no clinician-cognizable indication of the specific medical condition at the same time during the first time period before the intervention is administered; and

g. determining whether the second condition vectors are disposed within the content and comprise a clinician-cognizable vector pattern, which signifies that the patient, while having no clinician-cognizable indication of the specific medical condition, nonetheless has a heightened risk of the onset of the specific medical condition,

whereby the patient, while not having the specific medical condition, is advised of the heightened risk of the specific medical condition being caused by the administration of the intervention, and wherein the administration of the intervention is discontinued to minimize liability that might result from continued administration of the intervention.

77. The method of claim 76, wherein the intervention comprises a pharmaceutical drug administered to the patient.

78. The method of claim 76, further comprising, after step (h), the step of (i) discontinuing administration of the intervention to the patient.

79. A method for making a risk/benefit determination of a therapeutic intervention in a subject, the method comprising:

a. calculating first vectors based on incremental time-dependent changes in subject data corresponding to clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria that define the presence of the medical condition, the first vectors defining a first portion in a first n-dimensional space;

b. administrating to the subject a therapeutic intervention having a suspected adverse effect;

c. calculating second vectors based on incremental time-dependent changes in subject data corresponding to clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria that define the absence of the suspected adverse effect, the second vectors defining a second portion in a second n-dimensional space;

d. determining whether the first vectors comprise a first clinician-cognizable vector pattern, which signifies that the therapeutic intervention is providing the propensity for the onset of the diminution of the medical condition; and

e. determining whether the second vectors comprise a second clinician-cognizable vector pattern, which second clinician-cognizable vector pattern signifies that the therapeutic intervention is causing the risk of the onset of the adverse effect;

wherein the benefit provided from the therapeutic intervention is compared to the risk caused from the therapeutic intervention by comparing the respective presence or absence of the first and second clinician-cognizable vector patterns, and, when present, the respective sizes of any divergent vectors.

80. The method of claim 79, wherein the first or second clinician-cognizable vector patterns comprise divergent vectors.

81. The method of claim 79, wherein the first and second vectors are calculated from subject data using a non-parametric, non-linear, generalized dynamic regression analysis system.

82. The method of claim 81, wherein the non-parametric, non-linear, generalized dynamic regression analysis system is a regression model for an underlying population of stochastic processes represented by an ensemble of sample paths of the first and second time period vectors.

83. The method of claim 82, wherein the non-parametric, non-linear, generalized dynamic regression analysis system uses the general equation:

dY(t)=X(t)dB(t)+dM(t)

wherein Y(t) or dY(t) is the stochastic differential of a right-continuous sub-martingale, X(t) is an n×

p matrix of clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria, dB(t) is a p-dimensional vector of unknown regression functions, and dM(t) is a stochastic differential n-vector of local square-integrable martingales.

84. The method of claim 82, wherein the respective clinician cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria are external covariates.

85. The method of claim 82, wherein the respective clinician cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria are functions of previous outcomes of Y.

86. The method of claim 85, wherein the functions of previous outcomes of Y are auto-regressions.

87. The method of claim 82, wherein B(t) is an unknown parameter estimated by any acceptable statistical estimation procedure.

88. The method of claim 87, wherein the acceptable statistical estimation procedure is selected from the group consisting of:

the Generalized Nelson-Aalen Estimator, Bayesian estimation, the Ordinary Least Squares Estimator, the Weighted Least Squares Estimator, and the Maximum Likelihood Estimator.

89. The method of claim 79, wherein the first portion comprises a content that comprises a boundary, and the first clinician-cognizable vector pattern comprises a divergent vector comprising a direction and magnitude so as to extend towards the boundary signifying the heightened propensity for the onset of the diminution of the medical condition.

90. The method of claim 79, wherein the second portion comprises a content that comprises a boundary, and the second clinician-cognizable vector pattern comprises a divergent vector comprising a direction and magnitude so as to extend towards the boundary signifying the heightened risk of the onset of the adverse effect.

91. The method of claim 79, wherein the method is computer-based.

92. The method of claim 79, wherein the first and second vectors exhibit a stochastic noise process.

93. The method of claim 92, wherein the stochastic noise process is Brownian motion.

94. The method of claims 93, wherein the Brownian motion is constrained.

95. A database for determining whether a subject has a heightened risk of the onset of a specific medical condition, the database comprising:

a. data comprising an n-dimensional space corresponding to a respective n-number of clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria useful for diagnosing the medical condition, wherein data points disposed within a first portion of the n-dimensional space signify the absence of a clinician-cognizable indication of the specific medical condition, and data points disposed within a second portion of the n-dimensional space signify the presence of a clinician-cognizable indication of the medical condition; and

b. subject data corresponding to the respective clinician-cognizable physiological, pharmacological, pathophysiolbgical, or pathopsychological criteria for the subject, the subject data comprising;

(i) incremental time-dependent vectors, wherein first vectors disposed within the first portion of the n-dimensional space having a first clinician-cognizable pattern signify the absence of a clinician-cognizable indication of the specific medical condition, and second vectors having a second clinician-cognizable vector pattern signifying that the subject, while having no clinician-cognizable indication of the specific medical condition, nonetheless has a heightened risk of the onset of the medical condition.

96. The database of claim 95, wherein the first vectors pattern comprises Brownian motion.

97. The database of claim 95, the second vectors pattern comprises a toroidal pattern.

98. The database of claim 97, the toroidal pattern extending from the first vectors pattern.

99. The database of claim 95, the subject data comprising a plurality of LFTs.

100. The database of claim 95, the first vector pattern signifying the absence of hepatotoxicity.

101. The database of claim 95, the second vector pattern signifying a heightened risk of the onset of hepatotoxicity.

103. The database of claim 95, the second vector pattern comprising a visual format comprising divergent vectors from the first vector pattern.

104. A database determinative of a subject not having a heightened risk of the onset of a specific medical condition, the database comprising:

a. data comprising an n-dimensional space corresponding to a respective n-number of clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria useful for diagnosing the medical condition, wherein points disposed within a first portion of the n-dimensional space signify the absence of a clinician-cognizable indication of the specific medical condition, and points disposed within a second portion of the n-dimensional space signify the presence of a clinician-cognizable indication of the medical condition; and

b. subject data corresponding to the respective clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria for the subject, the subject data comprising incremental time-dependent vectors, wherein the vectors are disposed within the first portion of the n-dimensional space so as to signify the absence of a heightened risk of the onset of the medical condition.

105. The database of claim 104, the first motion vectors comprise Brownian motion.

106. The database of claim 105, wherein the Brownian motion vectors are restrained within the first portion by a pathodynamic restitution force.

107. A method for statistically determining the relative normality of a specific medical condition of an individual comprising the steps of:

a. defining parameters related to a medical condition;

b. obtaining reference data for the parameters from a plurality of members of a population;

c. determining, for each member of the population, a medical score by multivariate analysis of the respective reference data for each member;

d. determining a medical score distribution for the population, the medical score distribution signifying the relative probability that a particular medical score is statistically normal relative to the medical scores of the members of the population;

e. obtaining subject data for the parameters for an individual at a plurality of times over a time period;

f. determining medical scores for the individual for the plurality of times by multivariate analysis of the subject data;

g. comparing the medical scores of the individual over the time period to the medical score distribution of the population, whereby a divergence of the medical scores of the individual over the time period from the medical score distribution of the population indicates a decreased probability that the individual has a statistically normal medical condition relative to the population, and whereby a convergence of the medical scores of the individual over the time period towards the medical score distribution of the population indicates an increased probability that the individual has a statistically normal medical condition relative to the population.

108. The method of claim 107, wherein the medical condition is a healthy medical condition, whereby the divergence of the medical condition scores of the individual from the medical condition distribution of the population indicates a decreased probability that the individual has the healthy medical condition.

109. The method of claim 107, wherein the medical condition is defined as a healthy medical condition, whereby the convergence of the medical condition scores of the individual from the medical condition distribution of the population indicates an increased probability that the individual has the healthy medical condition.

110. The method of claim 107, wherein the medical condition is an unhealthy medical condition, whereby the divergence of the medical condition scores of the individual from the medical condition distribution of the population indicates an increased probability that the individual does not have the unhealthy medical condition.

111. The method of claim 107, wherein the medical condition is defined as an unhealthy medical condition, whereby the convergence of the medical condition scores of the individual from the medical condition distribution of the population indicates an increased probability that the individual has the unhealthy medical condition.

112. The method of claim 107, further comprising the steps of:

displaying a graph of at least one medical score for the individual, and

displaying at least one confidence interval for the medical score distribution.

113. The method of claim 111, wherein the confidence interval is at least a 90% confidence interval.

114. The method of claim 111, wherein step (g)(i) further comprises displaying a line connecting the at least one medical score for the individual.

115. The method of claim 113, wherein the line comprises an interpolation.

116. The method of claim 114, wherein the interpolation comprises a cubic spline interpolation.

117. The method of claim 111, further comprising the step of displaying graphs of the medical score for the individual at specific times in consecutive order as a moving image thereby showing the change in the medical score for the individual over time.

118. The method of claim 107, wherein the medical condition comprises liver function.

119. The method of claim 114, wherein the parameters comprise at least two selected from the group consisting of:

AST, ALT, GGT, total bilirubin, total protein, serum albumin, alkaline phosphatase, and lactate dehydrogenase.

120. The method of claim 118, wherein the medical condition score is an 8-dimensional calculation.

121. A method for statistically determining the relative normality of a specific medical condition comprising:

a. defining parameters related to a medical condition;

b. obtaining reference data for the parameters from a plurality of members of a population;

c. determining a parameter distribution for the population for each parameter, the parameter distribution signifying the probability that a particular data value for a parameter is normal relative to the reference data for the parameters from the population;

d. obtaining subject data for the parameters from an individual at a plurality of times in a time period; and

e. displaying a plurality of multi-dimensional graphs comparing (i) subject data for two or three parameters and (ii) a multi-dimensional parameter distribution for the two or three parameters, each graph displaying the subject data for the two or three parameters at a specific time in the time period, whereby a divergence of the subject data over time from the multi-dimensional parameter distribution indicates a decreasing probability that the individual is statistically normal relative to the population, and whereby a convergence of the subject data of the individual over time with the multi-dimensional parameter distribution indicates an increasing probability that the individual is statistically normal relative to the population.

122. The method of claim 121, wherein the plurality of graphs are displayed in time-consecutive order as a moving image.

123. The method of claim 121, wherein step (e) further comprises displaying a line between the subject data for the two or three parameters.

124. The method of claim 122, wherein the line comprises an interpolation.

125. The method of claim 123, wherein the interpolation comprises a cubic spline interpolation.

126. The method of claim 121, wherein the medical condition comprises liver function.

127. The method of claim 125, wherein the parameters comprise at least two selected from the group consisting of:

AST, ALT, GGT, total bilirubin, total protein, serum albumin, alkaline phosphatase, lactate dehydrogenase, and combinations thereof.

133. The method of claim 127, wherein the medical condition comprises a healthy medical condition, whereby a divergence of the medical condition scores of the individual from the medical condition distribution of the population indicates an decreased probability that the individual has the healthy medical condition.

134. The method of claim 127, wherein the medical condition comprises a healthy medical condition, whereby a convergence of the medical condition scores of the individual from the medical condition distribution of the population indicates an increased probability that the individual has the healthy medical condition.

135. The method of claim 127, wherein the medical condition comprises an unhealthy medical condition, whereby a divergence of the medical condition scores of the individual from the medical condition distribution of the population indicates an increased probability that the individual does not have the unhealthy medical condition.

136. The method of claim 127, wherein the medical condition comprises an unhealthy medical condition, whereby a convergence of the medical condition scores of the individual from the medical condition distribution of the population indicates an increased probability that the individual has the unhealthy medical condition.

138. The method of claim 127, wherein step (f) further comprises displaying graphs of the study data for multiple parameters for an individual subject at specific times in time-consecutive order as a moving image showing the change in the medical score for the individual over time.

139. The method of claim 127, wherein the specific medical condition comprises liver function.

140. The method of claim 138, wherein the parameters comprise at least two selected from the group consisting of:

AST, ALT, GGT, total bilirubin, total protein, serum albumin, alkaline phosphatase, lactate dehydrogenase, and combinations thereof.

141. The method of claim 127, wherein the medical score comprises an 8-dimensional calculation.

128. A system for statistically determining the relative normality of a specific medical condition in an individual comprising:

a. reference data comprising data for a plurality of members of a population for a plurality of parameters related to a medical condition, the reference data stored in a parameter data file;

b. study data comprising data from individual subjects for the plurality of parameters at a plurality of times in a time period, the study data stored in a study data file;

c. data definitions stored in a data definition file;

d. a user interface;

e. analysis software for determining;

(i) a medical score for each member of the population by multivariate analysis of their respective reference data,

(ii) medical scores over the time period for each individual subject by multivariate analysis of their respective study data,

(iii) a medical score distribution for the population, the medical score distribution signifying the relative probability that a particular medical score is statistically normal relative to the medical scores of the members of the population, and

(iv) multi-dimensional parameter distributions; and

f. display software for visualizing medical scores for at least one individual subject over time compared to the medical score distribution.

129. The system of claim 128, wherein the analysis software operates in a software runtime environment.

130. The system of claim 128, wherein the software runtime environment is Java.

131. The system of claim 128, wherein the data definition file comprises structured information identified by a markup language.

132. The system of claim 130, wherein the markup language is XML.

142. A method for statistically determining the relative normality of a specific medical condition of an individual comprising:

a. defining parameters related to a medical condition;

b. obtaining reference data for the parameters from a plurality of members of a population;

c. determining, for each member of the population, a medical score by multivariate analysis of the respective reference data for each member;

d. determining a medical score distribution for the population, the medical score distribution signifying the relative probability that a particular medical score is statistically normal relative to the medical scores of the members of the population;

e. obtaining subject data for the parameters for an individual at a plurality of times over a time period;

f. determining medical scores for the individual for the time period by multivariate analysis of the subject data;

g. comparing of the medical scores of the individual over the time period to the medical score distribution of the population, whereby a divergence of the medical scores of the individual over the time period away from the medical score distribution of the population indicates a decreased probability that the individual has a statistically normal medical condition relative to the population, and whereby a convergence of the medical scores of the individual over the time period towards the medical score distribution of the population indicates an increased probability that the individual has a statistically normal medical condition relative to the population.

143. A method for predicting whether a subject has a heightened risk of the onset of a specific medical condition, comprising a non-parametric, non-linear, generalized dynamic regression analysis system that uses the general equation:

$Y<br><br>\left(t\right)={\int <br><br>}_0^t<br><br>X<br><br>\left(s\right)<br><br>d<br><br>B<br><br>\left(s\right)+\mathrm{\Theta <br><br>}<br><br>\left(Z<br><br>\left(t\right),\mathrm{\Gamma <br><br>}<br><br>\left(t\right)\right)<br><br>W<br><br>\left(t\right)$

wherein the integrals are stochastic integrals;

Y(t) is the stochastic process being modeled;

X(s) is an n×

p matrix of the respective clinician-cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria;

dB(t) is a p-dimensional vector of unknown regression functions, and is the residual term, where

${\mathrm{\Theta <br><br>}}_i<br><br>\left(Z<br><br>\left(t\right),\mathrm{\Gamma <br><br>}<br><br>\left(t\right)\right)=\sqrt{\frac{1}{t}<br><br>{\int <br><br>}_0^t<br><br>Z_i<br><br>\left(s\right)<br><br>d<br><br>\mathrm{\Gamma <br><br>}<br><br>\left(s\right)}$ $\mathrm{and}$ $\mathrm{\Theta <br><br>}<br><br>\left(Z<br><br>\left(t\right),\mathrm{\Gamma <br><br>}<br><br>\left(t\right)\right)=\mathrm{diag}<br><br>\left({\mathrm{\Theta <br><br>}}_1<br><br>\left(Z<br><br>\left(t\right)\right),\mathrm{\Gamma <br><br>}<br><br>\left(t\right)\right),\mathrm{\dots <br><br>}<br><br>\text{\hspace{1em}<br><br>},{\mathrm{\Theta <br><br>}}_n<br><br>\left(Z<br><br>\left(t\right),\mathrm{\Gamma <br><br>}<br><br>\left(t\right)\right)<br><br>\text{)}.$

144. The method of claim 143, wherein the respective clinician cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria are external covariates.

145. The method of claim 143, wherein the respective clinician cognizable physiological, pharmacological, pathophysiological, or pathopsychological criteria are functions of previous outcomes of Y.

146. The method of claim 145, wherein the functions of previous outcomes of Y are auto-regressions.

147. The method of claim 143, wherein B(t) is an unknown parameter estimated by any acceptable statistical estimation procedure.

148. The method of claim 147, wherein the acceptable statistical estimation procedure is selected from the group consisting of:

the Generalized Nelson-Aalen Estimator, Baysesian estimation, the Ordinary Least Squares Estimator, the Weighted Least Squares Estimator, and the Maximum Likelihood Estimator.

149. A system for statistically determining the cost-benefit/cost-effectiveness of a specific analysis situation comprising:

a. reference data comprising data for a plurality of analysis individual members of a population for a plurality of parameters related to a specific analysis situation, the reference data stored in a parameter data file;

b. study data comprising data from individual situations for the plurality of parameters at a plurality of times in a time period, the study data stored in a study data file;

c. data definitions stored in a data definition file;

d. a user interface;

e. analysis software for determining;

(i) an analysis score for each member of the analysis population by multivariate analysis of their respective reference data,

(ii) analysis scores over the time period for each analysis individual member subject by multivariate analysis of their respective study data,

(iii) an analysis score distribution for the analysis population, the analysis score distribution signifying the relative probability that a particular analysis score is statistically normal relative to the analysis scores of the members of the analysis population, and

(iv) multi-dimensional parameter distributions; and

f. display software for visualizing analysis scores for at least one analysis individual subject over time compared to the analysis score distribution.

150. The system of claim 149, wherein the analysis software operates in a software runtime environment.