Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies.

Journal of Biomedical Semantics
Martijn G KerslootDerk L Arts

Abstract

Free-text descriptions in electronic health records (EHRs) can be of interest for clinical research and care optimization. However, free text cannot be readily interpreted by a computer and, therefore, has limited value. Natural Language Processing (NLP) algorithms can make free text machine-interpretable by attaching ontology concepts to it. However, implementations of NLP algorithms are not evaluated consistently. Therefore, the objective of this study was to review the current methods used for developing and evaluating NLP algorithms that map clinical text fragments onto ontology concepts. To standardize the evaluation of algorithms and reduce heterogeneity between studies, we propose a list of recommendations. Two reviewers examined publications indexed by Scopus, IEEE, MEDLINE, EMBASE, the ACM Digital Library, and the ACL Anthology. Publications reporting on NLP for mapping clinical text from EHRs to ontology concepts were included. Year, country, setting, objective, evaluation and validation methods, NLP algorithms, terminology systems, dataset size and language, performance measures, reference standard, generalizability, operational use, and source code availability were extracted. The studies' objectives were categorize...Continue Reading

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Citations

Mar 19, 2021·Frontiers in Artificial Intelligence·Jay CarriereMahdi Tavakoli
Aug 2, 2021·Computer Methods and Programs in Biomedicine·A W OlthofL J Cornelissen
Aug 7, 2021·Der Pathologe·Anna Melissa SchlitterIrene Esposito
Aug 15, 2021·Journal of Biomedical Informatics·Shikhar VashishthCarolyn P Rosé

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