Explainable Deep Contrastive Federated Learning System for Early Prediction of Clinical Status in Intensive Care Unit

Early identification of patients’ clinical status plays a critical role in intensive care unit (ICU) care. The increased adoption of electronic health records (EHRs) in the ICU creates prospects for deep learning (DL) application systems in this discipline. However, monitoring and prediction systems in the ICU encounter problems with security, alarm errors, and interpretation. This research presents deep contrastive federated learning (Deep-CFL), an approach that leverages explainable AI (XAI), CFL, and imbalanced supervised learning techniques to address these problems. CFL introduces an innovative approach to minimize the difference in local and global model prediction ability while increasing the gap in prediction performance of the current local model and its previous model in a communication round. When paired with imbalanced learning, this strategy substantially mitigates error alarm problems while ensuring data security. The XAI technique, specifically integrated gradient, is employed to refine the DL-based model architecture to enhance system interpretability. Extensive experiments and in-depth analyses across three significant clinical datasets highlight the superior performance of Deep-CFL over local and centralized learning-based approaches. The results involving
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patients admitted to Chonnam National University Hospital reveal that Deep-CFL, with an area under the receiver operating characteristics curve of 0.879, an area under the precision-recall curve of 0.886, and an average precision of 0.884, surpasses systems based on centralized learning while reducing the late alarm rate by up to 10.3%.