Belief-Space Control for Personalized Cancer Treatment via Active Inference

The paper presents a novel framework for cancer treatment using belief-space control through active inference, addressing the challenges of sequential decision-making with partial observability and patient heterogeneity. By modeling treatment as a belief-space planning problem, the approach derives an expected free-energy objective that integrates goal-directed control and information acquisition within measurement budgets. This framework, validated on real clinical data from the AACR Project GENIE Biopharma Collaborative dataset, shows promising results in patient categorization and treatment efficacy, which is critical for practitioners aiming to optimize personalized cancer therapies.