A Review of Intelligent System for Predicting Complications in Hemodialysis Patients Using Machine Learning Techniques
DOI:
https://doi.org/10.29304/jqcsm.2026.18.22639Keywords:
Hemodialysis,, Hypotension,, Dry weight,Abstract
Anesthesiology patients have CKD/ESRD as one of the comorbid condition and burdened with associated morbidity; despite continuous evolution in dialysis technology including mechanics, blood purification principle, membrane science, fluid therapy with but advances in long-term outcomes however remains meager especially during intra-dialysis period where patient may experience few abnormalities like hypotension/hypertension/arrhythmias/fluid overload/anemia instability due to vascular access damage potential contributory for morbidly predicable state and thus affecting outcome. Machine learning (ML) has the potential to enhance early detection of hemodynamic and biochemical deterioration with multidimensional clinical, laboratory, and statistical data from foreign agencies. The evidence from published machine learning research on dialysis-related complications, including blood pressure swings, dry weight monitoring, quality-of-life prediction, hospitalization risk, cardiac arrest, anemia management, and mortality prediction, is compiled in this review. In more than 30 studies, gradient boosting models (XGBoost/LightGBM) and deep learning architectures performed best, especially in predicting low and high systolic blood pressure [50–56]; simple_ linear statistical models performed worse, indicating that it is unrealistic to expect these models to capture complex nonlinear physiological relationships. Despite this compelling evidence, most studies have methodological shortcomings, such as retrospective single-center training sets. variable feature sets, a lack of external validation, poor interpretability, and underrepresentation of time-series signals. Multicenter cohorts, explainable AI, real-time streaming analytics, federated learning, and the integration of the Internet of Things in Healthcare. Platforms for providing Individualized adaptive hemodialysis therapy should be the focus of future research.
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