Proactive Prediction of Alarms in Hemodialysis Machines: A Comparative Evaluation of AI Models for Clinical Prognostic Support

Background/Objectives: Hemodialysis is a treatment for patients with kidney disease, yet it remains risk-prone due to potential technical or physiological instabilities. Dialysis machines rely on reactive safety mechanisms that trigger alarms only when predefined thresholds are reached. This often alerts clinical staff only after a complication has already occurred, contributing to potential delays in interventions. This study aims to develop a proactive prognostic framework capable of anticipating system instabilities before they transform into clinical adverse events.