Evidence map›Paper›PMID 31794016›Full record

ReviewJournal of the American Medical Informatics Association : JAMIA2020

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 and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 178 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
178citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

178 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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118 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Stephen WuSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Kirk RobertsSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Surabhi DattaSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Jingcheng DuSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Zongcheng JiSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Yuqi SiSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Sarvesh SoniSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Qiong WangSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Qiang WeiSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Yang XiangSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Bo ZhaoSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Hua XuSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.

Funding

Open Health Natural Language Processing CollaboratoryU01TR002062 · NCATS · MAYO CLINIC ROCHESTER · PI JIANG, XIAOQIAN, LIU, HONGFANG · 2017 to 2021
$7.6M
Natural Language Question Understanding for Electronic Health RecordsR00LM012104 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI ROBERTS, KIRK EDWARD · 2016 to 2018
$747k
NCATS NIH HHS U01 TR002062NLM NIH HHS R00 LM012104
6 · The paper itself

Abstract

objectiveThis 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

methodsWe 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.

resultsDL 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).

conclusionDeep 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.

Indexed as

Natural Language ProcessingBibliometricsDeep LearningElectronic Health RecordsHumansdeep learningelectronic health recordsmethodical, review, clinical textnatural language processing

Identifiers

PMID31794016
PMCPMC7025365

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.