DIAGNOSING INAPPARENT DISEASES FROM COMMON CLINICAL TESTS USING BAYESIAN ANALYSIS

• US 20090024332A1
• Filed: 06/17/2008
• Published: 01/22/2009
• Est. Priority Date: 05/01/2001
• Status: Active Grant

• Current Assignee: Quest Diagnostics Investments Incorporated (Quest Diagnostics Incorporated)
• Original Assignee: Quest Diagnostics Investments Incorporated (Quest Diagnostics Incorporated)
• Inventors: Maggio, Edward T., Billingsley, Frank, Kasten, Bernard, Karlov, Valeri I., Padilla, Carlos E.
• Granted Patent: US 8,068,993 B2
• Time in Patent Office: Days
• US Class Current: 702/19
• 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 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 50/80 for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
• 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

A system and method of diagnosing diseases from biological data is disclosed. A system for automated disease diagnostics prediction can be generated using a database of clinical test data. The diagnostics prediction can also be used to develop screening tests to screen for one or more inapparent diseases. The prediction method can be implemented with Bayesian probability estimation techniques. The system and method permit clinical test data to be analyzed and mined for improved disease diagnosis.

Claims

1. A method of processing test data, comprising:

determining an estimate for one or more hypothesis-conditional probability density functions p(x|H_k) for a set X of the test data conditioned on a set H of hypotheses relating to the test data;

determining a set of prior probability density functions p(H_k) for each hypothesis of the set H; and

determining a set of posterior test-conditional probability density functions p(H_k|x) for the hypotheses conditioned on a new data x;

wherein the p(x|H_i) estimates include a global estimate produced in accordance with the uncertainties in the statistical characteristics of the test data relating to each hypothesis-conditional pdf p(x|H_k).

2. A method as defined in claim 1, wherein the uncertainties in the statistical characteristics are specified as an ellipsoid about the test data for each hypothesis and each ellipsoid is defined by an m-dimensional ellipsoid E_q,k for each hypothesis H_k and is specified by:

E_q,k={x;

(x−

m_x,k)^TP_x,k<sup>−

1</sup>(x−

m_x,k)≦

μ

_q,k²}

where the m×

1 vector x is the argument in the space of test data, the m×

1 vector m_x,k is the mean (center) of each ellipsoid, the m×

m matrix P_x,k is a covariance matrix of the ellipsoid, and the scalar μ

_q,k² defines the size of the q-th ellipsoid, such that the global estimate of the hypothesis-conditional pdf is specified by;

{circumflex over (p)}_glob(x/H_k)=α

_q,k if xε

E_q,k∩

E_q-i,k(E_0,k=E_l,k),k=1, . . . , N

for a selected confidence interval parameter α

_q,k.

3. A method as defined in claim 1, wherein the hypothesis-conditional p(x|H_k) estimates further include a local estimate produced in accordance with a discrete neighbor counting process for a test data relative to the global estimate for the corresponding hypothesis-conditional pdf.

4. A method as defined in claim 3, wherein the local estimate for a hypothesis is specified as a probability that an observed vector of tests x and an associated discrete neighbor counting pattern {C_l,k(x)}, l=1, . . . , L_k, k=1, . . . , N might actually be observed, wherein the neighbor counting pattern comprises counting neighbors in the distance layers for each class:

{C_l,k}, l=1, . . . , L_k, wherein the integer C_l,k is the number of neighbors associated with the k-th hypothesis whose test values are distanced from a next test value within the l-th globally-transformed distance layer for the k-th class;

5. A method as defined in claim 4, wherein the selected k-th class of the test data corresponds to a selected training subset class of the test data.

6. A method as defined in claim 1, further including:

performing a training mode in which a training subset class of the test data is used to produce the hypothesis-conditional probability density functions p(x|H_k); and

performing a prediction mode in which a set of posterior probabilities is determined for the set H of hypotheses, wherein the hypothesis-conditional probability density functions p(x|h_k) are produced from the global estimates and from local estimates produced in accordance with a discrete neighbor counting process for a test data relative to the global estimate for the corresponding hypothesis-conditional pdf.

7. A method as defined in claim 6, wherein the local estimate for a hypothesis is specified as a probability that an observed vector of tests x and an associated discrete neighbor counting pattern {C_l,k(x)}, l=1, . . . , L_k, k=1. N might actually be observed, wherein the neighbor counting pattern comprises counting neighbors in the distance layers for each class:

{C_i,k}, l=1, . . . , L_k, wherein the integer C_l,k is the number of test elements associated with the

k-th hypothesis whose test values are distanced from a next test value within the l-th globally-transformed distance layer for the k-th class;

8. A method as defined in claim 7, wherein the selected k-th class of the test data corresponds to the training subset class of the test data.

9. The method of claim 1, wherein the posterior test-condition probabilities provide a diagnosis or risk of developing a disease or diseases.

10. The method of claim 1, wherein the data comprises biochemical data from a subject.

11. The method of claim 1, wherein the data comprises medical history data from a subject.

12. The method of claim 1, wherein the data comprises medical history data from a subject.

13. The method of claim 1, wherein the data comprises physiological data from a subject.

14. The method of claim 1, wherein the data comprises clinical data from a subject.

15. The method of claim 1, wherein the data comprises biochemical data from a subject.

16. The method of claim 1, wherein the data comprises medical history data from a subject.

17. The method of claim 1, wherein the data comprises medical history data from a subject.

18. The method of claim 1, wherein the data comprises physiological data from a subject.

19. The method of claim 1, wherein the data comprises clinical data from a subject.

20. The method of claim 1, wherein the diseases are selected from the group consisting of cardiovascular diseases, diabetes, neurodegenerative diseases, malignancies, ophthalmic diseases, blood diseases, respiratory diseases, endocrine diseases, bacterial, parasitic, fungal or viral infections, inflammatory diseases, to autoimmune diseases, reproductive diseases

21. A method for generating an a posteriori tree of possible diagnoses for a subject, the method comprising:

performing an analysis of test data for a population of individuals to whom a set of tests were administered comprising a matrix of pair-wise discriminations between diagnoses from a predetermined list of diagnoses;

performing a Bayesian statistical analysis to estimate a series of hypothesis-conditional probability density functions p(x|Hi) where a hypothesis Hi is one of a set H of the possible diagnoses;

determining a prior probability density function p(Hi) for each of the disease hypotheses Hi;

determining a posterior test-conditional probability density function p(Hi|x) for each of the hypotheses Hi test data records; and

generating a posterior tree of possible diagnoses for a test subject in accordance with test results for the test subject.

22. A method of diagnosing a disease condition of a patient, the method comprising:

receiving a set of population test data comprising test results for one or more patient tests performed on a population X of individuals;

estimating a hypothesis-conditional probability density function p(x|H1) where the hypothesis H1 relates to a diagnosis condition for a test patient x, and estimating a hypothesis-conditional probability density function p(x|H2) where the hypothesis H2 relates to a non-diagnosis condition for a test patient;

determining a prior probability density function p(H) for the each of the hypotheses H1 and H2;

determining a posterior test-conditional probability density function p(H|x) for each of the hypotheses H1 and H2 on the test data x; and

providing a diagnosis probability of a new patient for the H disease condition, based on the determined posterior test-conditional probability density unction p(H1|x) as compared to the posterior test-conditional probability density function p(H2|x) and one or more test results of the new patient.

23. The method of claim 22, wherein the data comprises biochemical data from a subject.

24. The method of claim 22, wherein the data comprises medical history data from a subject.

25. The method of claim 22, wherein the data comprises medical history data from a subject.

26. The method of claim 22, wherein the data comprises physiological data from a subject.

27. The method of claim 22, wherein the data comprises clinical data from a subject.

28. The method of claim 22, wherein the diseases are selected from the group consisting of cardiovascular diseases, diabetes, neurodegenerative diseases, malignancies, ophthalmic diseases, blood diseases, respiratory diseases, endocrine diseases, bacterial, parasitic, fungal or viral infections, inflammatory diseases, autoimmune diseases, reproductive diseases.

29. The method of claim 22, wherein the diseases are selected from selected from the group consisting of cancers.

30. A method of diagnosing a disease from data, comprising:

conducting a statistical analysis of the data in order to identify trends and dependencies among the data, wherein the data comprises biological data from a subject;

deriving a probabilistic model from the data, the probabilistic model being indicative of a probable disease diagnosis for a patient, wherein the disease is an inapparent disease.

31. A method as defined in claim 30, wherein the probabilistic model is derived using a discrete Bayesian analysis.

32. A method as defined in claim 30, further comprising compiling data into a database.

33. A method as defined in claim 30, further comprising an update step in which new data is convolved with the a priori probability of a discretized state vector of a hypothesis to generate the a posteriori probability of the hypothesis.

34. A method as defined in claim 33, further comprising a prediction step wherein trends in the data are captured via Markov chain models of the discretized state.

35. A method of claim 30, wherein the disease is cancer.

36. A method of claim 30, wherein the disease is ovarian cancer.

37. A method of claim 30, wherein the disease is colon cancer.

38. A method of claim 30, wherein the disease is hypertension.

39. A method of developing a test to screen for one or more inapparent diseases, comprising:

conducting a statistical analysis of the data in order to identify trends and dependencies among the data, wherein the data comprises biological data from a subject;

deriving a probabilistic model from the data, the probabilistic model being indicative of a probable disease diagnosis for a patient, wherein the probabilistic model is derived using a discrete Bayesian analysis;

identifying from among the input data, the data that contributes to the diagnosis; and

identifying the clinical or other input tests that generated the data that contributes to the diagnosis.

40. The method of claim 39, wherein the disease is an inapparent disease

41. A method of optimizing a clinical test for diagnosis, comprising conducting a statistical analysis of the data in order to identify trends and dependencies among the data, wherein the data comprises biological data from a subject;

deriving a probabilistic model from the data, the probabilistic model being indicative of a probable disease diagnosis for a patient, wherein the probabilistic model is derived using a discrete Bayesian analysis;

identifying from among the input data, the data that do not contributes the diagnosis;

eliminating the clinical tests that generate such data that do not contributes the diagnosis from the diagnosis protocol for the disease to thereby optimize the clinical test.

42. The method of claim 41, wherein the disease is an inapparent disease.

43. A program product for use in a computer that executes program steps recorded in a computer-readable media to perform a method of processing test data, the program product comprising:

a recordable media;

a plurality of computer-readable instructions executable by the computer to perform a method comprising;

determining an estimate for one or more hypothesis-conditional probability density functions p(x|H_k) for a set X of the test data conditioned on a set H of hypotheses relating to the test data;

determining a set of prior probability density functions p(H_k) for each hypothesis of the set H; and

determining a set of posterior test-conditional probability density functions p(H_k|x) for the hypotheses conditioned on a new data x;

wherein the p(x|H_i) estimates include a global estimate produced in accordance with the uncertainties in the statistical characteristics of the test data relating to each hypothesis-conditional pdf p(x|H_k).

44. A program product as defined in claim 43, wherein the uncertainties in the statistical characteristics are specified as an ellipsoid about the test data for each hypothesis and each ellipsoid is defined by m-dimensional ellipsoid E_q,k for each hypothesis H_k and is specified by:

E_q,k={x;

(x−

m_x,k)^TP_x,k<sup>−

1</sup>(x−

m_x,k)≦

μ

_q,k²}

where the m×

1 vector x is the argument in the space of test data, the m×

1 vector m_x,k is the mean (center) of each ellipsoid, the m×

m matrix P_x,k is a covariance matrix of the ellipsoid, and the scalar μ

_q,k² defines the size of the q-th ellipsoid, such that the global estimate of the hypothesis-conditional pdf is specified by;

{circumflex over (p)}_glob(x/H_k)=α

_q,k if xε

E_q,k∩

E_q-1,k(E_0,k=E_l,k),k=1, . . . , N

for a selected confidence interval parameter α

_q,k.

45. A program product as defined in claim 43, wherein the hypothesis-conditional p(x|H_k) estimates ether include a local estimate produced in accordance with a discrete neighbor counting process for a test data relative to the global estimate for the corresponding hypothesis-conditional pdf.

46. A program product as defined in claim 45, wherein the local estimate for a hypothesis is specified as a probability that an observed vector of tests x and an associated discrete neighbor counting pattern {C_l,k(x)}, l=1, . . . , L_k, k=1, . . . , N might actually be observed, wherein the neighbor counting pattern comprises counting neighbors in the distance layers for each class:

{C_i,k}, l=1, . . . , L_k, wherein the integer C_l,k is the number of neighbors associated with the k-th hypothesis whose test values are distanced from a next test value within the l-th globally-transformed distance layer for the k-th class;

47. A program product as defined in claim 46, wherein the selected k-th class of the test data corresponds to a selected training subset class of the test data.

48. A program product as defined in claim 43, further including:

performing a training mode in which a training subset class of the test data is used to produce the hypothesis-conditional probability density functions p(x|H_k); and

performing a prediction mode in which a set of posterior probabilities is determined for the set H of hypotheses, wherein the hypothesis-conditional probability density functions p(x|H_k) are produced from the global estimates and from local estimates produced in accordance with a discrete neighbor counting process for a test data relative to the global estimate for the corresponding hypothesis-conditional pdf.

49. A program product as defined in claim 48, wherein the local estimate for a hypothesis is specified as a probability that an observed vector of tests x and an associated discrete neighbor counting pattern {C_l,k(x)}, l=1, . . . , k=1, . . . N might actually be observed, wherein the neighbor counting pattern comprises counting neighbors in the distance layers for each class:

{C_l,k}, l=1, . . . , L_k, wherein the integer C_i,k is the number of test elements associated with the k-th hypothesis whose test values are distanced from a next test value within the l-th globally-transformed distance layer for the k-th class;

50. A program product as defined in claim 49, wherein the selected k-th class of the test data corresponds to the training subset class of the test data.

51. The program product of claim 43, wherein the posterior test-condition probabilities provide a diagnosis or risk of developing a disease or diseases.

52. The program product of claim 43, wherein the data comprises biochemical data from a subject.

53. The program product of claim 43, wherein the data comprises medical history data from a subject.

54. The program product of claim 43, wherein the data comprises medical history data from a subject.

55. The program product of claim 43, wherein the data comprises physiological data from a subject.

56. The program product of claim 43, wherein the data comprises clinical data from a subject.

57. The program product of claim 43, wherein the data comprises biochemical data from a subject.

58. The program product of claim 43, wherein the data comprises medical history data from a subject.

59. The program product of claim 43, wherein the data comprises medical history data from a subject.

60. The program product of claim 43, wherein the data comprises physiological data from a subject.

61. The program product of claim 43, wherein the data comprises clinical data from a subject.

62. The program product of claim 43, wherein the diseases are selected from the group consisting of cardiovascular diseases, diabetes, neurodegenerative diseases, malignancies, ophthalmic diseases, blood diseases, respiratory diseases, endocrine diseases, bacterial, parasitic, fungal or viral infections, inflammatory diseases, autoimmune diseases, reproductive diseases.