Abstract: A method and system for medical analytics implemented on a computer and designed to aid a medical professional in diagnosing one or more diseases afflicting a patient. In contrast to prior art, the present method is based on using clinical data (m) that excludes subjective qualities of and also excludes prevalence of the one or more diseases (i). The method uses a knowledge base that contains disease (i) models exhibiting clinical data (m). Clinical data present (j) in the patient are input into the computer. Then, clinical data present (j) are matched with clinical data (m) in the knowledge base to enable the computer to compose a differential diagnosis list of ruled in diagnoses (k), where k=1 . . . n, for each of the disease (i) models that exhibits at least one clinical datum (m) that matches at least one clinical datum present (j) in the patient.

Abstract: A method and system for medical analytics implemented on a computer and designed to aid a medical professional in diagnosing one or more diseases afflicting a patient. In contrast to prior art, the present method is based on using clinical data (m) that excludes subjective qualities of and also excludes prevalence of the one or more diseases (i). The method uses a knowledge base that contains disease (i) models exhibiting clinical data (m). Clinical data present (j) in the patient are input into the computer. Then, clinical data present (j) are matched with clinical data (m) in the knowledge base to enable the computer to compose a differential diagnosis list of ruled in diagnoses (k), where k=1 . . . n, for each of the disease (i) models that exhibits at least one clinical datum (m) that matches at least one clinical datum present (j) in the patient.

Abstract: This algorithm and corresponding computer program emulates the diagnostic reasoning of a clinician. Accurate and efficient, it concludes only those final diagnoses that agree with the diseases that actually afflict a patient. A differential diagnosis list is created and the probability of each diagnosis is calculated with a novel procedure that we call Mini-Max Procedure that uses the positive predictive value of clinical data present to increase probability and the sensitivity of clinical data absent to reduce probability. The probability of a diagnosis is considered equal to the maximum positive predictive value of all clinical data present that support the diagnosis, circumventing more complex and inaccurate prior art methods. The Mini-Max Procedure also identifies concurrent diseases. Bayes formula, because of its inability to process properly interdependent clinical data and concurrent diseases, is used with modifications.