Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients: Observational Cohort Study.

ABSTRACT:

Background

Implementing machine learning models to identify clinical deterioration in the wards is associated with decreased morbidity and mortality. However, these models have high false positive rates and only use structured data.

Objective

We aimed to compare models with and without information from clinical notes for predicting deterioration.

Methods

Adults admitted to the wards at the University of Chicago (development cohort) and University of Wisconsin-Madison (external validation cohort) were included. Predictors consisted of structured and unstructured variables extracted from notes as concept unique identifiers (CUIs). We parameterized CUIs in 5 ways: standard tokenization (ST), International Classification of Diseases rollup using tokenization (ICDR-T), Int

SUBMITTER: Kotula CA 

PROVIDER: S-EPMC12176310 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature