research-article

Natural language processing systems for capturing and standardizing unstructured clinical information

Authors:

Kory Kreimeyer,

Matthew Foster,

Abhishek Pandey,

Gwendolyn Halford,

Sandra F Jones,

Richard Forshee,

Mark Walderhaug,

Taxiarchis BotsisAuthors Info & Claims

Journal of Biomedical Informatics, Volume 73, Issue C

Pages 14 - 29

https://doi.org/10.1016/j.jbi.2017.07.012

Published: 01 September 2017 Publication History

Abstract

Display Omitted A literature review for clinical natural language processing systems.Over 7000 publications were reviewed in a multi-stage process.A final list of 71 natural language processing systems was identified.Each system was briefly summarized based on reviewed information. We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers. A total of 7149 records (after removing duplicates) were retrieved and screened, and 86 were determined to fit the review criteria. These papers contained information about 71 different clinical NLP systems, which were then analyzed. The NLP systems address a wide variety of important clinical and research tasks. Certain tasks are well addressed by the existing systems, while others remain as open challenges that only a small number of systems attempt, such as extraction of temporal information or normalization of concepts to standard terminologies. This review has identified many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated here will be important for prioritizing development of new approaches for clinical NLP.

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cover image Journal of Biomedical Informatics

Journal of Biomedical Informatics Volume 73, Issue C

September 2017

170 pages

ISSN:1532-0464

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Copyright © Elsevier Inc.

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Elsevier Science

San Diego, CA, United States

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Published: 01 September 2017

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Bergomi LBuonocore TAntonazzo PAlberghi LBellazzi RPreda LBortolotto CParimbelli E(2024)Reshaping free-text radiology notes into structured reports with generative question answering transformersArtificial Intelligence in Medicine10.1016/j.artmed.2024.102924154:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.artmed.2024.102924
da Silva DFröhlich WSchwertner MRigo S(2023)Clinical Oncology Textual Notes Analysis Using Machine Learning and Deep LearningIntelligent Systems10.1007/978-3-031-45389-2_10(140-153)Online publication date: 25-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-45389-2_10
Nagarajan RThirunavukarasu R(2022)A neuro-fuzzy based healthcare framework for disease analysis and predictionMultimedia Tools and Applications10.1007/s11042-022-12369-281:8(11737-11753)Online publication date: 18-Feb-2022
https://dl.acm.org/doi/10.1007/s11042-022-12369-2
Del Alamo JGuaman DGarcía BDiez A(2022)A systematic mapping study on automated analysis of privacy policiesComputing10.1007/s00607-022-01076-3104:9(2053-2076)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s00607-022-01076-3
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de Azevedo RRodrigues Jda Silva Reis MMoro CParaiso E(2018)Temporal Tagging of Noisy Clinical Texts in Brazilian PortugueseComputational Processing of the Portuguese Language10.1007/978-3-319-99722-3_24(231-241)Online publication date: 24-Sep-2018
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