SYSTEMS AND METHODS FOR DISEASE RISK PREDICTION FOR HIGH-RISK PATIENTS USING HIGHEST-K LOSS OPTIMIZED MACHINE LEARNING

Abstract: systems and methods predict disease risk in a sub-population based on characteristic data including demographic data and health data measured and collected from health monitoring devices. during training, a machine learning model receives biological outcomes for each subject of a population, and is trained using a resource limitation based loss function (“highest-k loss function”) using a soft sorting method to optimize accuracy of the machine learning model for various sub-populations of subjects who are at the highest risk for the biological outcomes. subsequent data on available resources in a hospital are fed to the trained model for determining biological outcomes for the high-risk sub-population and ranking them for optimizing monitoring, treatment, care, resource allocation.

Inventor(s): Sardar Ansari, Kevin R. Ward, Hongyi Yang, Alfred Hero

CPC Classification: G16H50/80 (for detecting, monitoring or modelling epidemics or pandemics, e.g. flu)

Search for rejections for patent application number 20250182910