A scalable approach for developing clinical risk prediction applications in different hospitals.
Journal:
Journal of biomedical informatics
Published Date:
Apr 20, 2021
Abstract
OBJECTIVE: Machine learning (ML) algorithms are now widely used in predicting acute events for clinical applications. While most of such prediction applications are developed to predict the risk of a particular acute event at one hospital, few efforts have been made in extending the developed solutions to other events or to different hospitals. We provide a scalable solution to extend the process of clinical risk prediction model development of multiple diseases and their deployment in different Electronic Health Records (EHR) systems.