Towards Accurate and Clinically Meaningful Summarization of Electronic Health Record Notes: A Guided Approach

Zhimeng Luo; Yuelyu Ji; Abhibha Gupta; Zhuochun Li; Adam Frisch; Daqing He

Abstract

Clinicians are usually under time pressure when they review patients' electronic health records (EHR), therefore, there are great benefits to providing clinicians high quality summarizations of patients' EHR. However, existing summarization algorithms cannot satisfy their needs. In this paper, we present a novel approach to summarize EHR notes using a guided summarization model. Our model integrates a structured template developed with a clinical domain expert, a Named Entity Recognition (NER) model and sentence classification model for guidance extraction, and a fact-checking metric for evaluating the generated summaries. We trained our model on a large de-identified EHR dataset. The results demonstrate that our guidance, which includes Chief Complaint (CC), NER, guidance from the History of Present Illness (HPI) section, and guidance from the Medical Decision Making (MDM) section, can significantly improve the performance of the models in generating accurate and clinically meaningful summaries. The Gsum (CNN) model with all the guidance aforementioned achieved the highest F1 score of 46.4, demonstrating the effectiveness of introducing precise and informative guidance to models from the general domain when the training data on the clinical domain is prohibitively sensitive and expensive. This work contributes to the ongoing efforts to automate the summarization of EHR notes, with the ultimate goal of improving healthcare delivery and patient outcomes.

Keywords: Electronic health records; named entity recognition; abstractive summarization; natural language processing; deep learning

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