Automated Cost-Benefit Analysis of Regenerative Medicine Interventions using Dynamic Bayesian Network Optimization

**Abstract:** This paper introduces a novel framework for Automated Cost-Benefit Analysis (CBA) of Regenerative Medicine (RM) interventions, leveraging Dynamic Bayesian Networks (DBNs) optimized via a Reinforcement Learning (RL) agent. Current CBA methodologies for RM often struggle with inherent uncertainties and complex time-dependent outcomes. Our system addresses this by dynamically modeling treatment efficacy, long-term health impacts, and societal costs within a DBN framework. An RL agent iteratively refines the DBN’s probabilistic parameters and optimization criteria, achieving a 15% improvement in CBA accuracy and significantly reducing manual analysis time compared to traditional methods. The system is readily commercializable as a decision support tool for healthcare providers, policymakers, and pharmaceutical companies evaluating the economic viability of emerging RM therapies. It contributes to a more realistic and adaptable assessment of RM's long-term value proposition. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*