An explainable real-time artificial intelligence -assisted system to reduce intradialytic hypotension
Cheng-Yen Lee1, Yi-Ting Chen2, Chung-Che Wang3
1Data Science Degree Program, National Taiwan University and Academia Sinica, Taipei, Taiwan; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Summary
An AI system accurately predicts intradialytic hypotension (IDH) during hemodialysis, reducing its incidence. This interpretable tool provides real-time decision support, improving patient outcomes.
Area of Science:
- Nephrology
- Artificial Intelligence
- Machine Learning
Background:
- Intradialytic hypotension (IDH) is a common hemodialysis complication with serious outcomes.
- Current AI models for IDH lack clinical applicability and interpretability.
- There is a need for integrated AI systems for real-time decision support in hemodialysis.
Purpose of the Study:
- To develop and validate an interpretable AI system for real-time prediction of IDH.
- To assess the impact of the AI system on IDH incidence.
- To provide clinicians with actionable, individualized insights for IDH management.
Main Methods:
- Analysis of 1.94 million hemodialysis records (2016-2023).
- Development of machine learning and time-series models, with CatBoost selected as the best performer.
- Integration of the AI model with SHapley Additive exPlanations (SHAP) into a real-time prediction dashboard.
Main Results:
- CatBoost model achieved high accuracy (AUC, sensitivity 96.05%, specificity 97.16%).
- Key predictors identified: systolic blood pressure and shock index.
- AI system implementation led to a significant decrease in IDH incidence (16.42% to 13.09%, P=0.03).
Conclusions:
- The developed AI system accurately predicts IDH with high interpretability.
- Explainable AI provides real-time, individualized decision support for hemodialysis.
- The AI system significantly reduced the incidence of intradialytic hypotension.
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