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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative

Nima Safaei¹, Babak Safaei², Seyedhouman Seyedekrami³

¹Department of Business Analytics and Information Systems, Tippie College of Business, University of Iowa, Iowa City, IA, United States of America.

|May 5, 2022

Summary

This summary is machine-generated.

This study introduces E-CatBoost, a machine learning model predicting Intensive Care Unit (ICU) patient mortality using limited data. Key features like age and vital signs improve early risk detection for better healthcare management.

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Area of Science:

Medical Informatics
Machine Learning in Healthcare
Critical Care Medicine

Background:

Improving Intensive Care Unit (ICU) management and healthcare systems is crucial.
Accurate and explainable mortality prediction models are needed for early risk identification and patient prioritization.
Existing models often require extensive data or lack interpretability.

Purpose of the Study:

To develop a highly accurate and efficient machine learning model for predicting ICU mortality using only the first 24 hours of patient data.
To identify critical risk factors influencing patient survival/death status.
To enhance the explainability of mortality predictions.

Main Methods:

Utilized supervised machine learning models, including CatBoost, and illness severity scoring systems for benchmarking.
Implemented explainability techniques such as SHAP, LIME, partial dependence, and individual conditional expectation plots.
Trained and validated models on the eICU-CRD v2.0 dataset (over 200,000 ICU admissions), stratified by twelve disease groups.

Main Results:

The proposed E-CatBoost model achieved high predictive performance, with Area Under the Receiver Operating Curve (AUROC) scores ranging from 0.83 to 0.91 across disease groups.
E-CatBoost demonstrated superior performance compared to baseline models, with AUROC scores 2-12% higher.
Identified age, heart rate, respiratory rate, blood urea nitrogen, and creatinine level as the most critical cross-disease predictors of mortality.

Conclusions:

The E-CatBoost model offers an accurate, efficient, and explainable approach to predicting ICU mortality using minimal initial patient data.
This tool can aid in early detection of high-risk patients, facilitating timely interventions and optimizing ICU resource allocation.
The identified key features provide valuable insights into patient prognosis and can inform clinical decision-making.