Deep learning in clinical natural language processing: a methodical review.

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 pop

SUBMITTER: Wu S 

PROVIDER: S-EPMC7025365 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature