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

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