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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. In a key step, the computer computes a probability P(k) for each of the ruled in diagnoses (k) with the aid of a mini-max procedure that overcomes prior art limitations of the Bayes formulation and permits the analytics method to consider concurrent and competing diagnoses (k). Furthermore, the method composes pairs of clinical data present (j) and absent (r) in the patient to aid the medical professional in evaluating diagnoses and determining the most cost-effective clinical data to collect for conducting an effective and rapid diagnostic quest.

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