AI-Driven Big Data Analytics for Personalized Treatment Planning
Keywords:
Big Data in Healthcare, Artificial Intelligence in Healthcare, Personalized Medicine, Predictive Analytics, Risk Stratification Models, Clinical Risk Scores, Causal Inference in Healthcare, Treatment Optimization, Clinical Decision Support Systems, Data-Driven Healthcare Models, Real-World Evidence (RWE), Prospective Clinical Studies, Healthcare Data Repositories, AI Model Evaluation, Evidence-Based Medicine, Health Data Analytics, Patient-Centric Care, Ethical AI in Healthcare, Institutional Review Board (IRB) Approval, Translational Healthcare AI.Abstract
Big data and AI are poised to transform healthcare due to their capacity for providing personalized predictions and treatment decisions for individuals. Such predictions, often termed risk scores, are commonly used in predictive modeling for risk stratification. Nevertheless, risk scores are only part of a comprehensive predictive analytics strategy for personalized health. Data-driven risk scores must inform the causal-inference models that determine the optimal treatment to maximize the long-term well-being of individuals. Together, these two components can augment clinical decision support for health providers.
Despite these advances, the clinical translation of AI-augmented analytics relies critically on real-world evidence derived from their prospective application in clinical practice. Nevertheless, few of the aforementioned AI efforts require evaluation through pilot studies that collect evidence of their practical value. Future work, therefore, focuses on developing a supporting architecture to maintain a real-time repository of prospectively collected evidence. Such work is informed by discussions with health domain experts and ethics approval from an institutional review board.
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