AI-powered Personalized Treatment Recommendation Framework for Improved Healthcare Outcomes
Ismail Rahman
Universiti Malaysia Pahang (UMP)
Abstract
Providing personalized and optimized treatment recommendations is a critical challenge in the healthcare industry. With the exponential growth of medical data and advancements in artificial intelligence (AI) technologies, there is a significant opportunity to develop innovative frameworks that can leverage this data to deliver tailored treatment plans for improved patient outcomes. This research paper presents an AI-powered personalized treatment recommendation framework that integrates diverse data sources, advanced machine learning models, and explainable AI techniques to generate personalized treatment recommendations. The proposed framework consists of four key components: 1) Data Aggregation and Preprocessing, 2) Predictive Modeling, 3) Treatment Optimization, and 4) Explainable Recommendations. The data aggregation module collects and integrates clinical data, patient demographics, genomic information, and real-world evidence from various sources. The predictive modeling component leverages this data to develop accurate prediction models for key health outcomes, such as treatment efficacy, adverse events, and disease progression. The treatment optimization module then applies multi-criteria decision analysis and reinforcement learning techniques to identify the optimal treatment plan for each patient, considering their unique characteristics and preferences. Finally, the explainable recommendations component provides interpretable insights into the rationale behind the personalized treatment recommendations, enabling better understanding and trust from both patients and healthcare providers. The performance of the proposed framework is evaluated using real-world datasets from various clinical domains, including oncology, cardiology, and chronic disease management. The results demonstrate significant improvements in treatment outcomes, reduced adverse events, and enhanced patient satisfaction compared to traditional, one-size-fits-all approaches. Additionally, the framework's ability to provide explainable recommendations is shown to improve patient engagement and shared decision-making between patients and clinicians. This research presents a comprehensive and scalable AI-powered framework that can revolutionize the healthcare industry by enabling personalized and optimized treatment recommendations, leading to better health outcomes, reduced costs, and improved patient experience. The framework's modular design and adaptability to diverse clinical domains make it a promising solution for the future of precision medicine.
Author Biography
Ismail Rahman, Universiti Malaysia Pahang (UMP)
