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

Research output: Contribution to conferencePaper

Original languageEnglish
Number of pages8
Publication statusAccepted/In press - 26 Feb 2019
EventSecond UK Healthcare Text Analytics Conference - Cardiff, United Kingdom
Duration: 24 Apr 201925 Apr 2019
http://healtex.org/healtac-2019/

Conference

ConferenceSecond UK Healthcare Text Analytics Conference
Abbreviated titleHealTAC 2019
CountryUnited Kingdom
CityCardiff
Period24/04/1925/04/19
Internet address

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.

Event

Second UK Healthcare Text Analytics Conference

24/04/1925/04/19

Cardiff, United Kingdom

Event: Conference

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