This paper is in the following e-collection/theme issue:

Viewpoints on and Experiences with Digital Technologies in Health (68) Adoption and Change Management of eHealth Systems (677) Machine Learning (1401) Electronic Health Records (971) Decision Support for Health Professionals (1147)

Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33044, first published .
A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma

A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma

A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma

Authors of this article:

Gang Luo1 Author Orcid Image
Article Authors Cited by (1) Tweetations (3) Metrics

Journals

  1. Naderalvojoud B, Curtin C, Yanover C, El-Hay T, Choi B, Park R, Tabuenca J, Reeve M, Falconer T, Humphreys K, Asch S, Hernandez-Boussard T. Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network. Journal of the American Medical Informatics Association 2024;31(5):1051 View