TECHNOLOGY TO AUTOMATICALLY IDENTIFY THE MOST RELEVANT HEALTH FAILURE RISK FACTORS

Abstract: a system includes: a processing circuit including a memory device coupled to a processor, the memory device configured to store instructions thereon that, when executed by the processor, cause the processor to: generate synthetic class data using minority class data to obtain balanced class data including the minority class data corresponding to patients with a health failure, the synthetic class data corresponding to the health failure, and majority class data corresponding to patients without the health failure; automatically reduce, using a machine learning classifier, risk factor variables for the health failure to a reduced set of risk factor variables based on the balanced class data; and execute the machine learning classifier using as input a reduced set of risk factor variable data for a patient corresponding to the reduced set of risk factor variables to generate a probability indicator of the health failure for the patient.

Inventor(s): Divine E. EDIEBAH, Hajime KUSANO, Ciaran A. BYRNE, Krishnankutty SUDHIR, Nick WEST

CPC Classification: G16H50/20 (for computer-aided diagnosis, e.g. based on medical expert systems)

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