{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T06:29:16Z","timestamp":1778826556746,"version":"3.51.4"},"reference-count":117,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2019,12,3]],"date-time":"2019-12-03T00:00:00Z","timestamp":1575331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["U01TR002062"],"award-info":[{"award-number":["U01TR002062"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R00LM012104"],"award-info":[{"award-number":["R00LM012104"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"UTHealth Innovation for Cancer Prevention Research Training Program Pre-doctoral Fellowship","award":["#RP160015"],"award-info":[{"award-number":["#RP160015"]}]},{"name":"UTHealth Innovation for Cancer Prevention Research Training Program Pre-doctoral Fellowship","award":["ME-2018C1-10963"],"award-info":[{"award-number":["ME-2018C1-10963"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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 \u201clong tail\u201d 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>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).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1093\/jamia\/ocz200","type":"journal-article","created":{"date-parts":[[2019,11,9]],"date-time":"2019-11-09T20:09:23Z","timestamp":1573330163000},"page":"457-470","source":"Crossref","is-referenced-by-count":408,"title":["Deep learning in clinical natural language processing: a methodical review"],"prefix":"10.1093","volume":"27","author":[{"given":"Stephen","family":"Wu","sequence":"first","affiliation":[{"name":"School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6525-5213","authenticated-orcid":false,"given":"Kirk","family":"Roberts","sequence":"additional","affiliation":[{"name":"School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, 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