Development and prospective evaluation of a machine learning model to predict vomiting among pediatric cancer and hematopoietic cell transplant patients.

ABSTRACT:

Purpose

Objectives were to develop a machine learning (ML) model based on electronic health record (EHR) data to predict the risk of vomiting within a 96-hour window after admission to the pediatric oncology and hematopoietic cell transplant (HCT) services using retrospective data and to evaluate the model prospectively in a silent trial.

Patients and methods

Admissions between 2018-06-02 to 2024-02-17 (retrospective) and 2024-05-09 to 2024-08-05 (prospective) to the oncology or HCT services were included. Data source was SEDAR, a curated and validated approach to deliver EHR data for ML. Prediction time was 08:30 the morning following admission. The outcome was any vomiting within 96 h following prediction time. We trained models using L2-regularized logistic regression, Li

SUBMITTER: Yan AP 

PROVIDER: S-EPMC12577177 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature