Diagnosing inapparent diseases from common clinical tests using bayesian analysis

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.

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.

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.

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|Hk) 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(Hk) for each hypothesis of the set H;
• and
• determining a set of posterior test-conditional probability density functions p(Hk|x) for the hypotheses conditioned on a new data x;
• wherein the p(x|Hi) 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|Hk).