Explainable machine learning model for predicting traumatic brain injury-induced coagulopathy in elderly patients: A multicenter cohort study.

ABSTRACT:

Background

Traumatic brain injury-induced coagulopathy (TBI-IC) in the elderly is a severe complication of traumatic brain injury (TBI) that leads to unfavorable outcomes. Currently, there are no reliable machine learning (ML) models available for early identification. This study comprehensively assessed routine clinical characteristics at admission to develop a robust, generalizable, and explainable ML model for estimating TBI-IC risk in the elderly and to identify modifiable factors.

Methods

This study included two cohorts: a derivation cohort (n = 484) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and a validation cohort from the eICU Collaborative Research Database (eICU-CRD) (n = 788). Feature selection utilized the Boruta algorithm, followed by the

SUBMITTER: Yang B 

PROVIDER: S-EPMC12858556 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature