A Hybrid AI-Powered Adaptive Framework for Personalized Patient Care and Outcome Optimization
Keywords:
Hybrid AI; personalized healthcare; clinical decision support; reinforcement learning; adaptive care; predictive analytics; precision medicineAbstract
Healthcare is advancing toward personalized, adaptive, real-time care. This work presents a hybrid AI framework that combines machine learning, expert systems, and clinician input to support individualized patient management. The system processes diverse health data, including electronic records, genomics, and sensor inputs, and continuously learns to adjust treatments dynamically. Implemented in Python and PHP, it features data preprocessing pipelines, a modular AI engine for risk prediction and decision optimization, and an interactive clinician interface. Validated through simulations and case studies, the framework shows improved predictive accuracy and health outcomes compared to static protocols, offering timely, personalized recommendations that adapt to patient responses. The system architecture is implemented using Python for AI algorithm development and PHP for seamless integration into web-based clinical workflows. Core components include robust data preprocessing pipelines, a modular AI engine (comprising risk prediction, decision optimization, and feedback learning modules), and an interactive clinician interface for interpretability and oversight. Mathematical models formalize the adaptive decision-making process, incorporating equations for training, optimization, and knowledge integration. It addresses key issues such as interoperability, security, and compliance, illustrating how intelligent, adaptive systems can enhance precision medicine and patient-centered care.
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