Assessing the Generalizability of a Clinical Machine Learning Model Across Multiple Emergency Departments.

ABSTRACT:

Objective

To assess the generalizability of a clinical machine learning algorithm across multiple emergency departments (EDs).

Patients and methods

We obtained data on all ED visits at our health care system's largest ED from May 5, 2018, to December 31, 2019. We also obtained data from 3 satellite EDs and 1 distant-hub ED from May 1, 2018, to December 31, 2018. A gradient-boosted machine model was trained on pooled data from the included EDs. To prevent the effect of differing training set sizes, the data were randomly downsampled to match those of our smallest ED. A second model was trained on this downsampled, pooled data. The model's performance was compared using area under the receiver operating characteristic (AUC). Finally, site-specific models were trained and teste

SUBMITTER: Ryu AJ 

PROVIDER: S-EPMC9062323 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature