Digital health · 2026
AutoML-driven ensemble learning for intradialytic hypotension prediction.
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Cheng Chih-Yang, Lin Yu-Chun, Tung Anna Nai-Yun, Hu Hsiang-Wei, Chang I-Chiu
Аннотация
OBJECTIVE: Intradialytic hypotension (IDH) is a common and serious complication during hemodialysis (HD), associated with increased morbidity and mortality. This study aims to develop an artificial intelligence (AI)-driven early warning system to predict IDH events and recommend real-time dialysis parameter adjustments to enhance patient safety.
MATERIALS AND METHODS: A total of 9,634 HD sessions from 178 patients were collected. Advanced feature engineering techniques were applied, including domain-specific variable generation, Synthetic Minority Oversampling Technique (SMOTE), and Recursive Feature Elimination with Cross-Validation (RFECV). An AutoML pipeline tested 100 hyperparameter combinations per run to tailor hospital-specific models. This approach enables rapid development of customized models optimized for institutional variability.
RESULTS: Among the evaluated models, CatBoost and LightGBM with Extra Trees achieved the highest performance. CatBoost yielded the best results with a ROC-AUC of 0.934 and an F1-macro score of 0.508. Feature selection and SMOTEEEN resampling further enhanced performance, improving the ROC-AUC to 0.949.
CONCLUSION: The implementation of AutoML for large-scale hyperparameter optimization enables the efficient development of hospital-specific AI models with minimal manual input. This approach supports scalable customization across diverse clinical settings. Our findings underscore the value of AI-powered prediction systems in facilitating early identification and management of intradialytic hypotension (IDH), thereby contributing to improved patient safety and hemodialysis outcomes.
MATERIALS AND METHODS: A total of 9,634 HD sessions from 178 patients were collected. Advanced feature engineering techniques were applied, including domain-specific variable generation, Synthetic Minority Oversampling Technique (SMOTE), and Recursive Feature Elimination with Cross-Validation (RFECV). An AutoML pipeline tested 100 hyperparameter combinations per run to tailor hospital-specific models. This approach enables rapid development of customized models optimized for institutional variability.
RESULTS: Among the evaluated models, CatBoost and LightGBM with Extra Trees achieved the highest performance. CatBoost yielded the best results with a ROC-AUC of 0.934 and an F1-macro score of 0.508. Feature selection and SMOTEEEN resampling further enhanced performance, improving the ROC-AUC to 0.949.
CONCLUSION: The implementation of AutoML for large-scale hyperparameter optimization enables the efficient development of hospital-specific AI models with minimal manual input. This approach supports scalable customization across diverse clinical settings. Our findings underscore the value of AI-powered prediction systems in facilitating early identification and management of intradialytic hypotension (IDH), thereby contributing to improved patient safety and hemodialysis outcomes.