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.
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


ConferenceSecond UK Healthcare Text Analytics Conference
Abbreviated titleHealTAC 2019
CountryUnited Kingdom
Internet address

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