Integrating Nonindividual Patient Features in Machine Learning Models of Hospital-Onset Bacteremia.

ABSTRACT:

Importance

Hospital-onset bacteremia and fungemia (HOB) are common and potentially preventable complications of hospital care.

Objective

To assess whether nonindividual patient features, which summarize interactions with other patients and health care workers (HCWs), can contribute to predictive and causal machine learning models for HOB.

Design, setting, and participants

This prognostic study included adult patients admitted to Barnes-Jewish Hospital, an academic hospital in St Louis, Missouri, in 2021. Analyses were developed between October 2023 and August 2024 and in April 2025.

Exposure

Individual patient features were extracted from electronic health records and used to engineer nonpatient features, including interactions with HCWs and direct or indirect

SUBMITTER: Vazquez Guillamet MC 

PROVIDER: S-EPMC12223889 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature