Deploying machine learning models in clinical settings: a real-world feasibility analysis for a model identifying adult-onset type 1 diabetes initially classified as type 2.

ABSTRACT:

Objective

This study evaluates the performance and deployment feasibility of a machine learning (ML) model to identify adult-onset type 1 diabetes (T1D) initially coded as type 2 on electronic medical records (EMRs) from a health information exchange (HIE). To our knowledge, this is the first evaluation of such a model on real-world HIE data.

Materials and methods

An existing ML model, trained on national US EMR data, was tested on a regional HIE dataset, after several adjustments for compatibility. A localized model retrained on the regional dataset was compared to the national model. Discrepancies between the 2 datasets' features and cohorts were also investigated.

Results

The national model performed well on HIE data (AUROC = 0.751; precision at 5% recall [PR5] = 2

SUBMITTER: Brusini I 

PROVIDER: S-EPMC12557313 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature