Journal of the Formosan Medical Association = Taiwan yi zhi · 2026
An explainable real-time artificial intelligence -assisted system to reduce intradialytic hypotension.
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Lee Cheng-Yen, Chen Yi-Ting, Wang Chung-Che, Wang Yong-Ci, Tan Xin-Hui, Lai Tai-Shuan, Lu Yen-Wen, Jang Jyh-Shing Roger, Chou Yu-Hsiang
Аннотация
BACKGROUND: Intradialytic hypotension (IDH) is a frequent complication in hemodialysis and is associated with adverse cardiovascular and neurological outcomes. Existing AI prediction models often lack real-world applicability, highlighting the need for clinically relevant, interpretable, and integrated AI systems to support real-time decision-making in hemodialysis care.
METHODS: We analyzed hemodialysis data from 2016 to 2023, incorporating demographics, dialysis settings, and high-frequency vital signs. IDH was defined by a systolic blood pressure drop accompanied by clinical interventions. Machine-learning and time-series models were developed. The best-performing model was integrated into a real-time prediction dashboard with individualized SHapley Additive exPlanations (SHAP) evaluation. This AI system was validated to compare the incidence of IDH with and without AI assistance.
RESULTS: A total of 1.94 million hemodialysis records from 1351 patients were analyzed. After threshold optimization, CatBoost achieved the highest area under the curve and the best sensitivity-specificity balance. SHAP analysis identified systolic blood pressure and the shock index as key predictors. The system demonstrated a sensitivity of 96.05% and a specificity of 97.16%. The final model was incorporated into a clinical dashboard that provided real-time risk estimation and actionable SHAP-based recommendations. The incidence of IDH decreased significantly from 16.42% to 13.09% after implementation of the AI system (P = 0.03).
CONCLUSION: The CatBoost-based AI system can predict IDH with high accuracy while providing individualized, interpretable, and real-time decision support. Application of this explainable AI system was associated with a significant reduction in the incidence of IDH.
METHODS: We analyzed hemodialysis data from 2016 to 2023, incorporating demographics, dialysis settings, and high-frequency vital signs. IDH was defined by a systolic blood pressure drop accompanied by clinical interventions. Machine-learning and time-series models were developed. The best-performing model was integrated into a real-time prediction dashboard with individualized SHapley Additive exPlanations (SHAP) evaluation. This AI system was validated to compare the incidence of IDH with and without AI assistance.
RESULTS: A total of 1.94 million hemodialysis records from 1351 patients were analyzed. After threshold optimization, CatBoost achieved the highest area under the curve and the best sensitivity-specificity balance. SHAP analysis identified systolic blood pressure and the shock index as key predictors. The system demonstrated a sensitivity of 96.05% and a specificity of 97.16%. The final model was incorporated into a clinical dashboard that provided real-time risk estimation and actionable SHAP-based recommendations. The incidence of IDH decreased significantly from 16.42% to 13.09% after implementation of the AI system (P = 0.03).
CONCLUSION: The CatBoost-based AI system can predict IDH with high accuracy while providing individualized, interpretable, and real-time decision support. Application of this explainable AI system was associated with a significant reduction in the incidence of IDH.