Name
Title | ||
|---|---|---|
Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches. |
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 systemu0027s 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. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Computation and Language | Journal |
Volume | Citations | PageRank |
abs/1903.03985 | 0 | 0.34 |
References | Authors | |
16 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Philip John Gorinski | 1 | 0 | 0.34 |
| Honghan Wu | 2 | 45 | 10.28 |
| Claire Grover | 3 | 729 | 100.15 |
| Richard Tobin | 4 | 145 | 14.83 |
| Conn Talbot | 5 | 0 | 0.34 |
| Heather C. Whalley | 6 | 46 | 5.08 |
| Catherine Sudlow | 7 | 2 | 2.66 |
| William Whiteley | 8 | 4 | 0.73 |
| Beatrice Alex | 9 | 237 | 25.59 |