Aligning prediction models with clinical information needs: infant sepsis case study.

ABSTRACT:

Objective

Sepsis recognition among infants in the Neonatal Intensive Care Unit (NICU) is challenging and delays in recognition can result in devastating consequences. Although predictive models may improve sepsis outcomes, clinical adoption has been limited. Our focus was to align model behavior with clinician information needs by developing a machine learning (ML) pipeline with two components: (1) a model to predict baseline sepsis risk and (2) a model to detect evolving (dynamic) sepsis risk due to physiologic changes. We then compared the performance of this two-component pipeline to a single model that combines all features reflecting both baseline risk and evolving risk.

Materials and methods

We developed prediction models (two-stage pipeline and a single model) using l

SUBMITTER: Cao L 

PROVIDER: S-EPMC11887542 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature