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Machine Learning for Intensive Care Unit Length-of-Stay Prediction: A Simulation-Based Approach to Bed Capacity
Sara Garber1,2, Yarema Okhrin1,3
1Department of Statistics and Data Science, Faculty of Business and Economics, University of Augsburg, Augsburg, Germany.
Summary
Machine learning (ML) models can improve intensive care unit (ICU) bed capacity management by predicting patient length-of-stay (LOS). A simulation study showed ML models enhance capacity control, with XGBoost outperforming logistic regression for better resource allocation.
Area of Science:
- Health Informatics
- Machine Learning in Healthcare
- Operations Research
Background:
- Machine learning (ML) models are increasingly used for healthcare predictions, but their operational impact on resource management, like intensive care unit (ICU) bed capacity, is under-researched.
- Traditional ML evaluation metrics often lack practical insights for healthcare decision-makers regarding resource allocation.
- Understanding the real-world implications of ML predictions for operational efficiency is crucial for healthcare management.
Purpose of the Study:
- To evaluate the impact of ML-driven length-of-stay (LOS) predictions on ICU bed capacity management.
- To compare the performance of different ML models (logistic regression and XGBoost) in a simulated ICU environment.
- To assess the practical utility of ML models beyond traditional performance measures for clinical decision support.
Main Methods:
- A simulation study was conducted using the HiRID dataset, comprising high-frequency data from over 33,000 patients.
- Two classification models, logistic regression (LR) and extreme gradient boosting (XGB), were applied to predict ICU LOS.
- ML model predictions were integrated into a simulation framework replicating real-world ICU bed management to assess practical implications.
Main Results:
- Both ML models improved ICU capacity control compared to baseline scenarios.
- XGBoost demonstrated superior performance over LR in the simulation, leading to slight underoccupancy.
- LR resulted in slight overoccupancy, highlighting the nuanced impact of different ML models on bed management.
Conclusions:
- Evaluating ML models within the context of specific healthcare operations, like ICU capacity management, is essential for practical application.
- Simulation-based approaches provide more relevant insights for healthcare practitioners than traditional performance metrics.
- This study bridges the gap between ML predictive accuracy and actionable clinical decision support for efficient resource management.
