DR. CARLOS FEDER, M.D.
Osteopathic Medicine at Tennessee Ln, Palo Alto, CA

2008017, Jul 17, 2008

Mar 13, 2008

12/075609

Carlos Feder - Palo Alto CA, US
Tomas Feder - Palo Alto CA, US

A61B 5/00

600300

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. The algorithm recommends at each diagnostic step, the best cost-benefit clinical datum next to investigate. Furthermore, the algorithm can simultaneously recommend several best cost-benefit clinical data, avoiding the need to contact the patient after the result of each single test is obtained. Heuristic parameters and abridged output files reduce the great number of best cost benefit clinical data recommended, without compromising the accuracy of the diagnostic procedure. Interactions of drugs and concurrent diseases with clinical data of the primary disease is detected, precluding ruling out of serious diseases due to this masking effect. Overlooking of important diagnoses is precluded by searching and processing diagnoses that are related to confirmed diagnoses. The algorithm diagnoses clinical forms of disease and complex clinical presentations, where disease, syndromes, complications, and other clinical entities coexist in a single patient. The algorithm processes efficiently synonyms of clinical data and diagnoses. The algorithm is straightforward, logical and mathematically simple; heuristic restrictions preclude excessive proliferation of clinical data and diagnoses. Because it is expressed in natural language, it is readily understandable and user friendly.

2009025, Oct 15, 2009

May 18, 2009

12/454479

Carlos Feder - Palo Alto CA, US
Tomas Feder - Palo Alto CA, US

G06Q 50/00, G06N 5/02

705 3, 705 2, 706 52

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.