Name
Abstract | ||
|---|---|---|
Objective: This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research. Materials and Methods: We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers. Results: DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) and word2vec embeddings (74.1%) were the most popular methods; the information extraction tasks of text classification, named entity recognition, and relation extraction were dominant (89.2%). However, there was a "long tail" of other methods and specific tasks. Most contributions were methodological variants or applications, but 20.8% were new methods of some kind. The earliest adopters were in the NLP community, but the medical informatics community was the most prolific. Discussion: Our analysis shows growing acceptance of deep learning as a baseline for NLP research, and of DL-based NLP in the medical community. A number of common associations were substantiated (eg, the preference of recurrent neural networks for sequence-labeling named entity recognition), while others were surprisingly nuanced (eg, the scarcity of French language clinical NLP with deep learning). Conclusion: Deep learning has not yet fully penetrated clinical NLP and is growing rapidly. This review highlighted both the popular and unique trends in this active field. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1093/jamia/ocz200 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Keywords | Field | DocType |
deep learning,natural language processing,electronic health records,methodical,review,clinical text | Knowledge management,Artificial intelligence,Deep learning,Medicine | Journal |
Volume | Issue | ISSN |
27 | 3 | 1067-5027 |
Citations | PageRank | References |
3 | 0.40 | 0 |
Authors | ||
12 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephen Wu | 1 | 50 | 6.61 |
| Kirk Roberts | 2 | 334 | 39.86 |
| Surabhi Datta | 3 | 3 | 0.40 |
| Jingcheng Du | 4 | 30 | 16.40 |
| Zongcheng Ji | 5 | 104 | 6.30 |
| Yuqi Si | 6 | 13 | 3.09 |
| Sarvesh Soni | 7 | 4 | 2.10 |
| Qiong Wang | 8 | 3 | 0.40 |
| Qiang Wei | 9 | 133 | 30.22 |
| Yang Xiang | 10 | 11 | 4.25 |
| Bo Zhao | 11 | 3 | 0.40 |
| Hua Xu | 12 | 323 | 32.99 |