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

arXiv: https://arxiv.org/abs/1903.03985 · HTML: https://mlsanity.com/paper/1903.03985/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2019.

## Built on by (most cited first, 8 of 8)

- [Does Synthetic Data Generation of LLMs Help Clinical Text Mining?](https://mlsanity.com/paper/2303.04360/) (2023)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes](https://mlsanity.com/paper/2205.05656/) (2022)
- [Collaborative Synthesis of Patient Records through Multi-Visit Health State Inference](https://mlsanity.com/paper/2312.14646/) (2023)
- [The Online Pivot: Lessons Learned from Teaching a Text and Data Mining Course in Lockdown, Enhancing online Teaching with Pair Programming and Digital Badges](https://mlsanity.com/paper/2105.07847/) (2021)
- [Neuradicon: operational representation learning of neuroimaging reports](https://mlsanity.com/paper/2107.10021/) (2021)
- [A Systematic Review of Natural Language Processing Applied to Radiology Reports](https://mlsanity.com/paper/2102.09553/) (2021)
- [Development of the user-friendly decision aid Rule-based Evaluation and Support Tool (REST) for optimizing the resources of an information extraction task](https://mlsanity.com/paper/2506.13177/) (2025)

## Abstract (arXiv)

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.