Comparison and interpretability of machine learning models to predict severity of chest injury.

Autor:	Kulshrestha S; Burn and Shock Trauma Research Institute, Loyola University Chicago, Maywood, Illinois, USA.; Department of Surgery, Loyola University Medical Center, Maywood, Illinois, USA., Dligach D; Center for Health Outcomes and Informatics Research, Health Sciences Division, Loyola University Chicago, Maywood, Illinois, USA.; Department of Public Health Sciences, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA.; Department of Computer Science, Loyola University Chicago, Chicago, Illinois, USA., Joyce C; Center for Health Outcomes and Informatics Research, Health Sciences Division, Loyola University Chicago, Maywood, Illinois, USA.; Department of Public Health Sciences, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA., Gonzalez R; Burn and Shock Trauma Research Institute, Loyola University Chicago, Maywood, Illinois, USA.; Department of Surgery, Loyola University Medical Center, Maywood, Illinois, USA., O'Rourke AP; Department of Surgery, University of Wisconsin, Madison, Wisconsin, USA., Glazer JM; Department of Emergency Medicine, University of Wisconsin, Madison, Wisconsin, USA., Stey A; Department of Surgery, Northwestern University, Chicago, Illinois, USA., Kruser JM; Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA., Churpek MM; Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA., Afshar M; Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA.
Jazyk:	angličtina
Zdroj:	JAMIA open [JAMIA Open] 2021 Mar 01; Vol. 4 (1), pp. ooab015. Date of Electronic Publication: 2021 Mar 01 (Print Publication: 2021).
DOI:	10.1093/jamiaopen/ooab015
Abstrakt:	Objective: Trauma quality improvement programs and registries improve care and outcomes for injured patients. Designated trauma centers calculate injury scores using dedicated trauma registrars; however, many injuries arrive at nontrauma centers, leaving a substantial amount of data uncaptured. We propose automated methods to identify severe chest injury using machine learning (ML) and natural language processing (NLP) methods from the electronic health record (EHR) for quality reporting. Materials and Methods: A level I trauma center was queried for patients presenting after injury between 2014 and 2018. Prediction modeling was performed to classify severe chest injury using a reference dataset labeled by certified registrars. Clinical documents from trauma encounters were processed into concept unique identifiers for inputs to ML models: logistic regression with elastic net (EN) regularization, extreme gradient boosted (XGB) machines, and convolutional neural networks (CNN). The optimal model was identified by examining predictive and face validity metrics using global explanations. Results: Of 8952 encounters, 542 (6.1%) had a severe chest injury. CNN and EN had the highest discrimination, with an area under the receiver operating characteristic curve of 0.93 and calibration slopes between 0.88 and 0.97. CNN had better performance across risk thresholds with fewer discordant cases. Examination of global explanations demonstrated the CNN model had better face validity, with top features including "contusion of lung" and "hemopneumothorax." Discussion: The CNN model featured optimal discrimination, calibration, and clinically relevant features selected. Conclusion: NLP and ML methods to populate trauma registries for quality analyses are feasible. (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu