Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.

ABSTRACT:

Aims

To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals, collected within 24 h of ICU admission, for traumatic brain injury (TBI) patient outcomes.

Methods

Using data from TBI patients in the multi-center eICU database, we focused on in-hospital mortality, neurological status based on the Glasgow Coma Score (mGCS) motor subscore at discharge, and prolonged ICU stay (PLOS). Three machine learning (ML) models were developed, utilizing EHR features, PTS signals collected 24 h after ICU admission, and their combination. External validation was performed using the MIMIC III dataset, and interpretability was enhanced using the Shapley Additive Explanations (SHAP) algorithm.

Results

SUBMITTER: Ding R 

PROVIDER: S-EPMC11228354 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature