Development and Validation of a Class Imbalance-Resilient Cardiac Arrest Prediction Framework Incorporating Multiscale Aggregation, ICA and Explainability.

Objective: Despite advancements in artificial intelligence (AI) for predicting cardiac arrest (CA) with multivariate time-series vital signs data, existing models continue to face significant problems, particularly concerning balance, efficiency, accuracy, and explainability. While neural networks have been proposed to extract multiscale features from raw data in various applications, to our knowledge, no work has utilized multiscale feature extraction, specifically for diagnostic CA prediction. This paper presents a new framework that tackles these difficulties by utilizing multiscale feature aggregation via Independent Component Analysis (ICA).