Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks.

ABSTRACT:

Objective

Development of electronic health records (EHR)-based machine learning models for pediatric inpatients is challenged by limited training data. Self-supervised learning using adult data may be a promising approach to creating robust pediatric prediction models. The primary objective was to determine whether a self-supervised model trained in adult inpatients was noninferior to logistic regression models trained in pediatric inpatients, for pediatric inpatient clinical prediction tasks.

Materials and methods

This retrospective cohort study used EHR data and included patients with at least one admission to an inpatient unit. One admission per patient was randomly selected. Adult inpatients were 18 years or older while pediatric inpatients were more than 28 days and les

SUBMITTER: Lemmon J 

PROVIDER: S-EPMC10654865 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature