Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches
This work addresses the problem of extracting entities from EHRs for healthcare professionals, but it is incremental as it compares existing methods without introducing new techniques.
This paper compared rule-based, deep learning, and transfer learning approaches for Named Entity Recognition (NER) on brain imaging reports from stroke patients in Electronic Health Records, finding that a hand-crafted system was the most accurate, while machine learning offered a feasible alternative when manual resources are limited.
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.