analiza aiqbench
This paper proposes a novel application of active inference frameworks to optimize personalized cancer treatment plans by modeling the problem as belief-space planning under measurement constraints. By utilizing real clinical data from AACR Project GENIE, it demonstrates how AI can simultaneously categorize patient heterogeneity and maximize efficacy while adhering to strict medical resource budgets. The work represents significant progress in applying advanced machine learning techniques to solve complex sequential decision-making problems within oncology.
Belief-Space Control for Personalized Cancer Treatment via Active Inference
arXiv:2606.10376v2 Announce Type: replace Abstract: Cancer treatment is at the core a sequential decision-making problem with partial observability, latent patient heterogeneity, and explicit constraints on the budget for medical measurements. Unlike standard Reinforcement Learning (RL) approaches that control state trajectories, cancer treatments permanently modify patients' transition dynamics, changing how states evolve over time. We model cancer treatment as a belief-space planning problem using active inference, deriving an expected free-energy objective that unifies goal-directed control and information acquisition under measurement budgets without. We implement this framework using real clinical cancer data from the AACR Project GENIE Biopharma Collaborative dataset. Results on clinical data demonstrate a simultaneous patient categorization and high treatment efficacy, under real measurement and treatment constraints.
Abstract:Cancer treatment is at the core a sequential decision-making problem with partial observability, latent patient heterogeneity, and explicit constraints on the budget for medical measurements. Unlike standard Reinforcement Learning (RL) approaches that control state trajectories, cancer treatments permanently modify patients' transition dynamics, changing how states evolve over time. We model cancer treatment as a belief-space planning problem using active inference, deriving an expected free-energy objective that unifies goal-directed control and information acquisition under measurement budgets without. We implement this framework using real clinical cancer data from the AACR Project GENIE Biopharma Collaborative dataset. Results on clinical data demonstrate a simultaneous patient categorization and high treatment efficacy, under real measurement and treatment constraints.
Artificial Intelligence (cs.AI); Information Theory (cs.IT)
arXiv:2606.10376 [cs.AI]
(or arXiv:2606.10376v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.10376 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 207 cuvinte
