Deep learning in clinical natural language processing: a methodical review
The University of Texas Health Science Center at Houston
Abstract
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.
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.
Citation impact
- FWCI
- 29.89
- Percentile
- 100%
- References
- 90
Authors
12- SWStephen WuCorresponding
The University of Texas Health Science Center at Houston
- KRKirk Roberts
The University of Texas Health Science Center at Houston
- SDSurabhi Datta
The University of Texas Health Science Center at Houston
- JDJingcheng Du
The University of Texas Health Science Center at Houston
- ZJZongcheng Ji
The University of Texas Health Science Center at Houston
Topics & keywords
- Artificial intelligence
- Natural language processing
- Computer science
- Deep learning
- Context (archaeology)
- Information extraction
- Named-entity recognition
- Word2vec
- Quality Education