Enhancing Predictive Healthcare Using AI-Driven Early Warning Systems

Abstract

This research proposes an AI-driven early warning system to predict patient deterioration in real-time using electronic health records (EHRs) and wearable devices. Leveraging deep learning techniques, such as recurrent neural networks (RNNs) for sequential data and convolutional neural networks (CNNs) for pattern recognition, the system adapts dynamically through reinforcement learning. Evaluation strategies include retrospective and prospective studies in clinical settings, measuring prediction accuracy and impact on patient outcomes. If successful, this system has the potential to save lives, reduce ICU admissions, and transform healthcare into a proactive, data-driven field.