Machine Learning-Based Prediction of ICU Readmissions in Intracerebral Hemorrhage Patients: Insights from the MIMIC Databases

TL;DR

This study develops machine learning models using MIMIC ICU data to accurately predict ICU readmission risk in intracerebral hemorrhage patients, aiding clinical decisions and resource management.

Contribution

It introduces a predictive framework employing multiple machine learning algorithms for ICU readmission in ICH patients based on comprehensive clinical data.

Findings

Models achieved high AUROC scores indicating strong predictive performance

Key predictors include demographic, clinical, and laboratory features

The framework can assist in clinical decision-making and resource allocation

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.