WO2002087431B1 - Diagnosing inapparent diseases from common clinical tests using bayesian analysis - Google Patents

Diagnosing inapparent diseases from common clinical tests using bayesian analysis

Info

Publication number
WO2002087431B1
WO2002087431B1 PCT/US2002/014138 US0214138W WO02087431B1 WO 2002087431 B1 WO2002087431 B1 WO 2002087431B1 US 0214138 W US0214138 W US 0214138W WO 02087431 B1 WO02087431 B1 WO 02087431B1
Authority
WO
WIPO (PCT)
Prior art keywords
data
diseases
subject
disease
data comprises
Prior art date
Application number
PCT/US2002/014138
Other languages
French (fr)
Other versions
WO2002087431A1 (en
Inventor
Valeri I Karlov
Carlos E Padilla
Edward T Maggio
Frank Billingsley
Bernard Kasten
Original Assignee
Structural Bioinformatics Inc
Quest Diagnostics Invest Inc
Valeri I Karlov
Carlos E Padilla
Edward T Maggio
Frank Billingsley
Bernard Kasten
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Structural Bioinformatics Inc, Quest Diagnostics Invest Inc, Valeri I Karlov, Carlos E Padilla, Edward T Maggio, Frank Billingsley, Bernard Kasten filed Critical Structural Bioinformatics Inc
Publication of WO2002087431A1 publication Critical patent/WO2002087431A1/en
Publication of WO2002087431B1 publication Critical patent/WO2002087431B1/en

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Epidemiology (AREA)
  • Data Mining & Analysis (AREA)
  • Primary Health Care (AREA)
  • Databases & Information Systems (AREA)
  • Pathology (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)
  • Medical Treatment And Welfare Office Work (AREA)
  • Medicines That Contain Protein Lipid Enzymes And Other Medicines (AREA)

Abstract

A system and method of diagnosing diseases from biological data is disclosed. A system for automated disease diagnosis 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

AMENDED CLAIMS
[received by the International Bureau on 18 November 2002 (18.11.02); original claims 1-62 amended (8 pages)]
where nk is the total number of data records in a selected k -th class and the index
i runs over all these data records.
8.' A method as defined in claim 7, wherein the selected k-i 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.
13. The method of claim 1 , wherein the data comprises physiological data from a subject.
14. The method of claim 1, wherem the data comprises clinical data from a subject. 111
15. The method of claim 6, wherein the data comprises biochemical data from a subject.
16. The method of claim 6, wherein the data comprises medical history data from a subject.
18. The method of claim 6, wherein the data comprises physiological data from a subject.
1 . The method of claim 6, wherein the data comprises clinical data from a subject.
20. The method of claim 1, wherem 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, and reproductive diseases.
21. A method for generating an a posteriori tree of possible diagnoses for a subject, the method comprising: 112
determining a posterior test-conditional probability density function p(H|x) for each of the hypotheses HI 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 function p(Hl |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.
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, 113
malignancies, ophthalmic diseases, blood diseases, respiratory diseases, endocrine diseases, bacterial, parasitic, fungal or viral infections, inflammatory diseases, autoimmune diseases, and reproductive diseases.
29. The method of claim 22, wherein the diseases are 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; and 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 priori probability of a discretized state vector of a hypothesis to generate the a posteriori probability of the hypothesis. 114
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 35, wherein the disease is ovarian cancer.
37. A method of claim 35, 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 115
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; and eliminating the clinical tests that generate such data that do not contributes to 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; and 116
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, wherem 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.
3. 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 chnical data from a subject.
57. The program product of claim 48, wherein the data comprises biochemical data from a subject. 117
59. The program product of claim 48, wherein the data comprises medical history data from a subject.
60. The program product of claim 48, wherein the data comprises physiological data from a subject.
61. The program product of claim 48, wherein the data comprises clinic l 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, and reproductive diseases.
PCT/US2002/014138 2001-05-01 2002-05-01 Diagnosing inapparent diseases from common clinical tests using bayesian analysis WO2002087431A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US28799101P 2001-05-01 2001-05-01
US60/287,991 2001-05-01

Publications (2)

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WO2002087431A1 WO2002087431A1 (en) 2002-11-07
WO2002087431B1 true WO2002087431B1 (en) 2003-01-03

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