A Clinical Decision Support System for Edge/Cloud ICU Readmission Model Based on Particle Swarm Optimization, Ensemble Machine Learning, and Explainable Artificial Intelligence

ICU readmission is usually associated with an increased number of hospital death. Predicting readmission helps to reduce such risks by avoiding early discharge, providing appropriate intervention, and planning for patient placement after ICU discharge. Unfortunately, ICU scores such as the simplified acute physiology score (SAPS) and Acute Physiology and Chronic Health (APACHE) could help predict mortality or evaluate illness severity. Still, it is ineffective in predicting ICU readmission. This study introduces a clinical monitoring fog-computing-based system for remote prognosis and monitoring of intensive care patients. This proposed monitoring system uses the advantages of machine learning (ML) approaches for generating a real-time alert signal to doctors for supplying e-healthcare, accelerating decision-making, and monitoring and controlling health systems. The proposed system includes three main layers. First, the data acquisition layer, in which we collect the vital signs and lab tests of the patient’s health conditions in real-time. Then, the fog computing layer processes. The results are then sent to the cloud layer, which offers sizable storage space for patient healthcare. Demographic data, lab tests, and vital signs are aggregated from the MIMIC III dataset for 10,465 patients. Feature selection methods: Genetic algorithm (GA) and practical swarm optimization (PSO) are used to choose the optimal feature subset from detests. Moreover, Different traditional ML models, ensemble learning models, and the proposed stacking models are applied to full features and selected features to predict readmission after 30 days of ICU discharge. The proposed stacking models recorded the highest performance compared to other models. The proposed stacking ensemble model with selected features by POS achieved promising results (accuracy = 98.42, precision = 98.42, recall = 98.42, and F1-Score = 98.42), compared to full features and selected features. We also, provide model explanations to ensure efficiency, effectiveness, and trust in the developed model through local and global explanations.