Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches

Philip John Gorinski; Honghan Wu; Claire Grover; Richard Tobin; Conn Talbot; Heather Whalley; Cathie Sudlow; William Whiteley; Beatrice Alex

arXiv · 1903.03985

Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches

Philip John Gorinski·Honghan Wu·Claire Grover·Richard Tobin·Conn Talbot·Heather Whalley·Cathie Sudlow·William Whiteley·Beatrice Alex

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Abstract

This work investigates multiple approaches to Named Entity Recognition (NER) for text in Electronic Health Record (EHR) data. In particular, we look into the application of (i) rule-based, (ii) deep learning and (iii) transfer learning systems for the task of NER on brain imaging reports with a focus on records from patients with stroke. We explore the strengths and weaknesses of each approach, develop rules and train on a common dataset, and evaluate each system's performance on common test sets of Scottish radiology reports from two sources (brain imaging reports in ESS -- Edinburgh Stroke Study data collected by NHS Lothian as well as radiology reports created in NHS Tayside). Our comparison shows that a hand-crafted system is the most accurate way to automatically label EHR, but machine learning approaches can provide a feasible alternative where resources for a manual system are not readily available.

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Philip John Gorinski, Honghan Wu, Claire Grover, Richard Tobin, Conn Talbot, Heather Whalley, Cathie Sudlow, William Whiteley, Beatrice Alex. 2019-03-10. Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches. https://arxiv.org/abs/1903.03985

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