Systems and Methods for Medical Topic Discovery Based on Large-Scale Machine Learning

Abstract

A machine learning system that includes a plurality of machine learning processors, maintains a topic matrix that represents the relevancies or measures of prevalence of a plurality of medical topics among a plurality of clinical documents. Each processor in the system is configured to determine at least one local sufficient factor group for a document included in the plurality of documents, and to send the at least one local sufficient factor group to one or more other processors in the system. Each processor is further configured to receive at least one remote sufficient factor group from another processor in the system, and to process the local sufficient factor group together with the remote sufficient factor group to obtain the topic matrix. The remote sufficient factor group or groups are determined by other processors in the system for another document included in the plurality of documents.

Dependencies are inferred from the claim text.