用主动推理建模癌症治疗，兼顾疗效与检测成本
Belief-Space Control for Personalized Cancer Treatment via Active Inference
- 基于信念空间规划，统一控制与信息获取
- 在真实检测预算下实现患者分型与高治疗效果
- 适合个性化医疗与有限检测资源场景
癌症治疗本质上是一个具有部分可观测性、患者异质性隐变量及检测预算约束的序列决策问题。与传统强化学习控制状态轨迹不同，癌症治疗会永久改变患者的动态转移规律。本文采用主动推理框架，将癌症治疗建模为信念空间规划问题，推导出融合目标控制与信息获取的期望自由能目标函数，无需额外设计。基于AACR Project GENIE Biopharma Collaborative数据集的真实临床数据进行验证，结果表明在实际测量与治疗约束下，可同时实现患者分类与高效治疗。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。