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.
|Number of pages||8|
|Publication status||Accepted/In press - 26 Feb 2019|
|Event||Second UK Healthcare Text Analytics Conference - Cardiff, United Kingdom|
Duration: 24 Apr 2019 → 25 Apr 2019
|Conference||Second UK Healthcare Text Analytics Conference|
|Abbreviated title||HealTAC 2019|
|Period||24/04/19 → 25/04/19|