Predicting the Mortality of ICU Patients by Topic Model with Machine-Learning Techniques
Chih-Chou Chiu1, Chung-Min Wu1, Te-Nien Chien2
1Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan.
Healthcare (Basel, Switzerland)
|June 24, 2022
Summary
This study shows that semi-structured clinical data improves intensive care unit (ICU) patient mortality prediction. The gradient boosting machine learning model demonstrated superior performance in forecasting patient outcomes.
Area of Science:
- * Critical care medicine
- * Health informatics
- * Machine learning in healthcare
Background:
- * Predicting patient vital signs and mortality in intensive care units (ICUs) is crucial for reducing mortality and treatment costs.
- * Electronic health record (EHR) data is commonly used for mortality prediction, but semi-structured data (diagnosis, reports) is underutilized.
- * Early mortality prediction can significantly aid clinical decision-making in ICUs.
Purpose of the Study:
- * To investigate the utility of semi-structured clinical data for predicting ICU patient mortality.
- * To compare the performance of various machine learning models in mortality prediction using this data.
- * To identify the most effective model for developing a clinical decision support system.
Main Methods:
- * Utilized data from the Medical Information Mart for Intensive Care III (MIMIC-III) database, including 46,520 ICU patients.
- * Employed Latent Dirichlet Allocation (LDA) to classify topics within semi-structured data (diagnosis, inspection reports).
- * Compared five machine learning models: Classification and Regression Trees (CART), Logistic Regression (LR), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF), and Gradient Boosting (GB).
Main Results:
- * Semi-structured clinical data contains valuable information for critical clinical decisions.
- * The Gradient Boosting (GB) model achieved the highest Area Under the Receiver Operating Characteristic Curve (AUROC) at 0.9280, with 93.16% specificity and 83.25% sensitivity.
- * Other models showed strong performance: RF (AUROC 0.9096), LR (AUROC 0.8987), MARS (AUROC 0.8935), and CART (AUROC 0.8511).
Conclusions:
- * Semi-structured clinical data significantly enhances the prediction of ICU patient mortality.
- * The Gradient Boosting model is the most effective among the tested machine learning algorithms for this task.
- * Findings can inform the development of a clinically useful decision support system for ICUs.
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