Developing and validating a natural language processing algorithm to extract preoperative cannabis use status documentation from unstructured narrative clinical notes.

Autor:	Sajdeya R; Department of Epidemiology, College of Public Health & Health Professions & College of Medicine, University of Florida, Gainesville, Florida, USA., Mardini MT; Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA., Tighe PJ; Department of Anesthesiology, College of Medicine, University of Florida, Gainesville, Florida, USA., Ison RL; Department of Anesthesiology, College of Medicine, University of Florida, Gainesville, Florida, USA., Bai C; Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA., Jugl S; Department of Pharmaceutical Outcomes & Policy, Center for Drug Evaluation and Safety (CoDES), University of Florida, Gainesville, Florida, USA., Hanzhi G; Department of Biostatistics, University of Florida, Gainesville, Florida, USA., Zandbiglari K; Department of Pharmaceutical Outcomes & Policy, Center for Drug Evaluation and Safety (CoDES), University of Florida, Gainesville, Florida, USA., Adiba FI; Department of Pharmaceutical Outcomes & Policy, Center for Drug Evaluation and Safety (CoDES), University of Florida, Gainesville, Florida, USA., Winterstein AG; Department of Pharmaceutical Outcomes & Policy, Center for Drug Evaluation and Safety (CoDES), University of Florida, Gainesville, Florida, USA., Pearson TA; Department of Epidemiology, College of Public Health & Health Professions & College of Medicine, University of Florida, Gainesville, Florida, USA., Cook RL; Department of Epidemiology, College of Public Health & Health Professions & College of Medicine, University of Florida, Gainesville, Florida, USA., Rouhizadeh M; Department of Pharmaceutical Outcomes & Policy, Center for Drug Evaluation and Safety (CoDES), University of Florida, Gainesville, Florida, USA.
Jazyk:	angličtina
Zdroj:	Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2023 Jul 19; Vol. 30 (8), pp. 1418-1428.
DOI:	10.1093/jamia/ocad080
Abstrakt:	Objective: This study aimed to develop a natural language processing algorithm (NLP) using machine learning (ML) techniques to identify and classify documentation of preoperative cannabis use status. Materials and Methods: We developed and applied a keyword search strategy to identify documentation of preoperative cannabis use status in clinical documentation within 60 days of surgery. We manually reviewed matching notes to classify each documentation into 8 different categories based on context, time, and certainty of cannabis use documentation. We applied 2 conventional ML and 3 deep learning models against manual annotation. We externally validated our model using the MIMIC-III dataset. Results: The tested classifiers achieved classification results close to human performance with up to 93% and 94% precision and 95% recall of preoperative cannabis use status documentation. External validation showed consistent results with up to 94% precision and recall. Discussion: Our NLP model successfully replicated human annotation of preoperative cannabis use documentation, providing a baseline framework for identifying and classifying documentation of cannabis use. We add to NLP methods applied in healthcare for clinical concept extraction and classification, mainly concerning social determinants of health and substance use. Our systematically developed lexicon provides a comprehensive knowledge-based resource covering a wide range of cannabis-related concepts for future NLP applications. Conclusion: We demonstrated that documentation of preoperative cannabis use status could be accurately identified using an NLP algorithm. This approach can be employed to identify comparison groups based on cannabis exposure for growing research efforts aiming to guide cannabis-related clinical practices and policies. (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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