Explainable machine learning for predicting neurological outcome in hemorrhagic and ischemic stroke patients in critical care.

ABSTRACT:

Aim

The objective of this study is to develop accurate machine learning (ML) models for predicting the neurological status at hospital discharge of critically ill patients with hemorrhagic and ischemic stroke and identify the risk factors associated with the neurological outcome of stroke, thereby providing healthcare professionals with enhanced clinical decision-making guidance.

Materials and methods

Data of stroke patients were extracted from the eICU Collaborative Research Database (eICU-CRD) for training and testing sets and the Medical Information Mart for Intensive Care IV (MIMIC IV) database for external validation. Four machine learning models, namely gradient boosting classifier (GBC), logistic regression (LR), multi-layer perceptron (MLP), and random forest (RF), w

SUBMITTER: Wei H 

PROVIDER: S-EPMC11194386 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature