Machine Learning-Based Prediction of ICU Readmissions in Intracerebral Hemorrhage Patients: Insights from the MIMIC Databases
Journal:
arXiv
Published Date:
Jan 2, 2025
Abstract
Intracerebral hemorrhage (ICH) is a life-risking condition characterized by
bleeding within the brain parenchyma. ICU readmission in ICH patients is a
critical outcome, reflecting both clinical severity and resource utilization.
Accurate prediction of ICU readmission risk is crucial for guiding clinical
decision-making and optimizing healthcare resources. This study utilized the
Medical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV) databases,
which contain comprehensive clinical and demographic data on ICU patients.
Patients with ICH were identified from both databases. Various clinical,
laboratory, and demographic features were extracted for analysis based on both
overview literature and experts' opinions. Preprocessing methods like imputing
and sampling were applied to improve the performance of our models. Machine
learning techniques, such as Artificial Neural Network (ANN), XGBoost, and
Random Forest, were employed to develop predictive models for ICU readmission
risk. Model performance was evaluated using metrics such as AUROC, accuracy,
sensitivity, and specificity. The developed models demonstrated robust
predictive accuracy for ICU readmission in ICH patients, with key predictors
including demographic information, clinical parameters, and laboratory
measurements. Our study provides a predictive framework for ICU readmission
risk in ICH patients, which can aid in clinical decision-making and improve
resource allocation in intensive care settings.
