Deep learning in clinical natural language processing: a methodical review. (3rd December 2019)

• Record Type: Journal Article
• Authors: Wu, Stephen; Roberts, Kirk; Datta, Surabhi; Du, Jingcheng; Ji, Zongcheng; Si, Yuqi; Soni, Sarvesh; Wang, Qiong; Wei, Qiang; Xiang, Yang; Zhao, Bo; Xu, Hua
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 3(2020)
• Page Start: 457
• Page End: 470
• Publication Date: 2019-12-03
• Subjects: deep learning -- natural language processing -- electronic health records -- methodical, review, clinical text; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocz200
• Languages: English
• ISSNs: 1067-5027

Abstract

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.