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

TL;DR

This paper compares rule-based, deep learning, and transfer learning methods for Named Entity Recognition in Electronic Health Records, focusing on brain imaging reports related to stroke, highlighting their strengths and limitations.

Contribution

It provides a comprehensive comparison of rule-based and machine learning approaches for NER in EHRs, including development, training, and evaluation on real-world datasets.

Findings

Rule-based system achieved highest accuracy.

Machine learning approaches are viable alternatives.

Performance varies depending on resource availability.

Abstract

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.