Developing a machine learning model to detect diagnostic uncertainty in clinical documentation.

Autor: Marshall TL; Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.; Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA., Nickels LC; Digital Scholarship Center, University of Cincinnati Libraries and College of Arts and Sciences, Cincinnati, Ohio, USA.; AI for All Lab, Digital Futures Program, University of Cincinnati, Cincinnati, Ohio, USA., Brady PW; Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.; Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA.; James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA., Edgerton EJ; Digital Scholarship Center, University of Cincinnati Libraries and College of Arts and Sciences, Cincinnati, Ohio, USA.; AI for All Lab, Digital Futures Program, University of Cincinnati, Cincinnati, Ohio, USA., Lee JJ; Digital Scholarship Center, University of Cincinnati Libraries and College of Arts and Sciences, Cincinnati, Ohio, USA.; AI for All Lab, Digital Futures Program, University of Cincinnati, Cincinnati, Ohio, USA., Hagedorn PA; Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.; Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA.; Department of Information Services, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
Jazyk: angličtina
Zdroj: Journal of hospital medicine [J Hosp Med] 2023 May; Vol. 18 (5), pp. 405-412. Date of Electronic Publication: 2023 Mar 15.
DOI: 10.1002/jhm.13080
Abstrakt: Background and Objective: Diagnostic uncertainty, when unrecognized or poorly communicated, can result in diagnostic error. However, diagnostic uncertainty is challenging to study due to a lack of validated identification methods. This study aims to identify distinct linguistic patterns associated with diagnostic uncertainty in clinical documentation.
Design, Setting and Participants: This case-control study compares the clinical documentation of hospitalized children who received a novel uncertain diagnosis (UD) diagnosis label during their admission to a set of matched controls. Linguistic analyses identified potential linguistic indicators (i.e., words or phrases) of diagnostic uncertainty that were then manually reviewed by a linguist and clinical experts to identify those most relevant to diagnostic uncertainty. A natural language processing program categorized medical terminology into semantic types (i.e., sign or symptom), from which we identified a subset of these semantic types that both categorized reliably and were relevant to diagnostic uncertainty. Finally, a competitive machine learning modeling strategy utilizing the linguistic indicators and semantic types compared different predictive models for identifying diagnostic uncertainty.
Results: Our cohort included 242 UD-labeled patients and 932 matched controls with a combination of 3070 clinical notes. The best-performing model was a random forest, utilizing a combination of linguistic indicators and semantic types, yielding a sensitivity of 89.4% and a positive predictive value of 96.7%.
Conclusion: Expert labeling, natural language processing, and machine learning methods combined with human validation resulted in highly predictive models to detect diagnostic uncertainty in clinical documentation and represent a promising approach to detecting, studying, and ultimately mitigating diagnostic uncertainty in clinical practice.
(© 2023 Society of Hospital Medicine.)
Databáze: MEDLINE
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