  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.
