Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network.

ABSTRACT:

Background

Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability.

Methods

Electronic health records (EHRs) from 4 countries (United States, United Kingdom, Finland, and Korea) were mapped to the OMOP Common Data Model (CDM), 2008-2019. Machine learning (ML) models were developed to pre

SUBMITTER: Naderalvojoud B 

PROVIDER: S-EPMC11031239 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature