TY - JOUR
AU - Xu,, Hua
AB - Downloaded from https://academic.oup.com/jamia/article-abstract/27/3/457/5651084 by guest on 19 February 2020 Journal of the American Medical Informatics Association, 27(3), 2020, 457–470 doi: 10.1093/jamia/ocz200 Advance Access Publication Date: 3 December 2019 Review Review Deep learning in clinical natural language processing: a methodical review Stephen Wu, Kirk Roberts , Surabhi Datta, Jingcheng Du, Zongcheng Ji, Yuqi Si, Sarvesh Soni, Qiong Wang, Qiang Wei , Yang Xiang, Bo Zhao, and Hua Xu School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA Corresponding Author: Stephen Wu, PhD, School of Biomedical Informatics, The University of Texas Health Science Cen- ter at Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, USA (wu.stephen.t@gmail.com) Received 3 July 2019; Revised 15 October 2019; Editorial Decision 18 October 2019; Accepted 9 November 2019 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 meth- ods, 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 
TI - Deep learning in clinical natural language processing: a methodical review
JF - Journal of the American Medical Informatics Association
DO - 10.1093/jamia/ocz200
DA - 2020-03-01
UR - https://www.deepdyve.com/lp/oxford-university-press/deep-learning-in-clinical-natural-language-processing-a-methodical-1CMxR1fSEn
SP - 457
VL - 27
IS - 3
DP - DeepDyve
ER -