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

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 generaliz

SUBMITTER: Rockenschaub P 

PROVIDER: S-EPMC11469625 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature