Semantic Expansion of Clinician Generated Data Preferences for Automatic Patient Data Summarization

Abstract

Patient Electronic Health Records (EHRs) typically contain a substantial amount of data, which can lead to information overload for clinicians, especially in high-throughput fields like radiology. Thus, it would be beneficial to have a mechanism for summarizing the most clinically relevant patient information pertinent to the needs of clinicians. This study presents a novel approach for the curation of clinician EHR data preference information towards the ultimate goal of providing robust EHR summarization. Clinicians first provide a list of data items of interest across multiple EHR categories. Since this data is manually dictated, it has limited coverage and may not cover all the important terms relevant to a concept. To address this problem, we have developed a knowledge-driven semantic concept expansion approach by leveraging rich biomedical knowledge from the UMLS. The approach expands 1094 seed concepts to 22,325 concepts with 92.69% of the expanded concepts identified as relevant by clinicians.