Identifying Repetitive Portions of Clinical Notes and Generating Summaries Pertinent to Treatment of a Patient Based on the Identified Repetitive Portions

摘要

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a repetitive portion identification and weighting engine. A machine learning model is trained for weighting repetitive portions of patient electronic medical records (EMRs). A repetitive portion identification component applies a plurality of templates to clinical notes of a patient EMR to identify one or more candidate portions that match at least one of the plurality of templates. A content analysis component performs content analysis on the one or more candidate portions to determine whether each given candidate portion is relevant. A weighting component assigns a relative weight to each given candidate portion based on relevance. A cognitive summary graphical user interface (GUI) generation component generates cognitive summary reflecting at least a subset of the one or more candidate portions of the patient EMR. The mechanism outputs the cognitive summary in a GUI to a user.