The Impact of Multi-Institution Datasets on the Generalizability of Machine Learning Prediction Models in the ICU

Author:

Rockenschaub Patrick123ORCID,Hilbert Adam1,Kossen Tabea1,Elbers Paul4,von Dincklage Falk5,Madai Vince Istvan26,Frey Dietmar1

Affiliation:

1. Charité Lab for Artificial Intelligence in Medicine (CLAIM), CharitéUniversitätsmedizin Berlin, Berlin, Germany

2. QUEST Center for Responsible Research, Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany.

3. Institute of Clinical Epidemiology, Public Health, Health Economics, Medical Statistics and Informatics, Medical University of Innsbruck, Innsbruck, Austria.

4. Department of Intensive Care Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.

5. Department of Anesthesia, Intensive Care, Emergency and Pain Medicine, Universitätsmedizin Greifswald, Greifswald, Germany.

6. Faculty of Computing, Engineering and the Built Environment, School of Computing and Digital Technology, Birmingham City University, Birmingham, United Kingdom.

Abstract

Objectives: To evaluate the transferability of deep learning (DL) models for the early detection of adverse events to previously unseen hospitals. Design: Retrospective observational cohort study utilizing harmonized intensive care data from four public datasets. Setting: ICUs across Europe and the United States. Patients: Adult patients admitted to the ICU for at least 6 hours who had good data quality. Interventions: None. Measurements and Main Results: Using carefully harmonized data from a total of 334,812 ICU stays, we systematically assessed the transferability of DL models for three common adverse events: death, acute kidney injury (AKI), and sepsis. We tested whether using more than one data source and/or algorithmically optimizing for generalizability during training improves model performance at new hospitals. We found that models achieved high area under the receiver operating characteristic (AUROC) for mortality (0.838–0.869), AKI (0.823–0.866), and sepsis (0.749–0.824) at the training hospital. As expected, AUROC dropped when models were applied at other hospitals, sometimes by as much as –0.200. Using more than one dataset for training mitigated the performance drop, with multicenter models performing roughly on par with the best single-center model. Dedicated methods promoting generalizability did not noticeably improve performance in our experiments. Conclusions: Our results emphasize the importance of diverse training data for DL-based risk prediction. They suggest that as data from more hospitals become available for training, models may become increasingly generalizable. Even so, good performance at a new hospital still depended on the inclusion of compatible hospitals during training.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Reference26 articles.

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