Belief-Space Control for Personalized Cancer Treatment via Active Inference

• URL: http://arxiv.org/abs/2606.10376v3
• Date: Wed, 17 Jun 2026 07:43:15 GMT
• Title: Belief-Space Control for Personalized Cancer Treatment via Active Inference
• Abstract summary: We model cancer treatment as a belief-space planning problem using active inference.<n>We implement this framework using real clinical cancer data from the AACR Project GENIE Biopharma Collaborative dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Confounder Detection via Treatment Intent: A New Observational Study Design [69.50062870487349]
  We introduce a new study design called confounder detection via treatment intent.<n>The idea is to query a human expert who makes treatment decisions, and ask them to compare pairs of units.<n>We show empirical evidence that electronic health records collected in ICUs are subject to unobserved confounding.
  arXiv  Detail & Related papers  (2026-05-26T00:41:47Z)
• From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction [61.12883122613684]
  This Review focuses on intervention-aware disease trajectory modeling in clinical AI--methods estimating patient-specific longitudinal disease evolution and assessing trajectory changes under alternative treatments.<n>We organize the field around six linked components: three decision tasks (factual forecasting, counterfactual estimation, policy evaluation) and three data-generating mechanisms (disease evolution, treatment assignment, observation process) that determine identifiability.<n>We present the first unified framework bridging forecasting, counterfactual trajectories, and policy evaluation across discrete/continuous time, explicitly addressing treatment assignment, time-varying confounding, and observation bias.
  arXiv  Detail & Related papers  (2026-05-16T10:45:26Z)
• Identifying and Characterising Response in Clinical Trials: Development and Validation of a Machine Learning Approach in Colorectal Cancer [0.45835414225547183]
  Precision medicine promises to transform health care by offering individualised treatments that dramatically improve clinical outcomes.<n>Current approaches are limited to static measures of treatment success, neglecting the repeated measures found in most clinical trials.<n>Our approach combines the concept of partly conditional modelling with treatment effect estimation based on the Virtual Twins method.<n>Performance was evaluated using synthetic data and applied to clinical trials examining the effectiveness of panitumumab to treat metastatic colorectal cancer.
  arXiv  Detail & Related papers  (2026-02-28T18:00:26Z)
• CLARITY: Medical World Model for Guiding Treatment Decisions by Modeling Context-Aware Disease Trajectories in Latent Space [49.74032713886216]
  CLARITY is a medical world model that forecasts disease evolution directly within a structured latent space.<n>It explicitly integrates time intervals (temporal context) and patient-specific data (clinical context) to model treatment-conditioned progression as a smooth, interpretable trajectory.
  arXiv  Detail & Related papers  (2025-12-08T20:42:10Z)
• Censoring-Aware Tree-Based Reinforcement Learning for Estimating Dynamic Treatment Regimes with Censored Outcomes [4.877686100899469]
  Censoring-Aware Tree-Based Reinforcement Learning (CA-TRL) is a novel framework to address the complexities associated with censored data.<n>We demonstrate its effectiveness through extensive simulations and real-world applications using the SANAD epilepsy dataset.
  arXiv  Detail & Related papers  (2025-03-09T16:53:09Z)
• Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data [0.05025737475817938]
  We propose a modular machine learning framework designed for personalized counterfactual cancer treatment suggestions. The framework is tailored to address critical challenges inherent in data-driven cancer research. Our method aims to empower clinicians with a reality-centric decision-support tool.
  arXiv  Detail & Related papers  (2024-02-19T14:54:20Z)
• Optimal discharge of patients from intensive care via a data-driven policy learning framework [58.720142291102135]
  It is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions. A data-driven approach is used to derive a parsimonious, discrete state space representation that captures a patient's physiological condition.
  arXiv  Detail & Related papers  (2021-12-17T04:39:33Z)
• Health improvement framework for planning actionable treatment process using surrogate Bayesian model [1.2468700211588881]
  This study proposes a novel framework to plan treatment processes in a data-driven manner. A key point of the framework is the evaluation of the "actionability" for personal health improvements.
  arXiv  Detail & Related papers  (2020-10-30T06:02:49Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)
• Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations [114.16762407465427]
  We introduce the Counterfactual Recurrent Network (CRN) to estimate treatment effects over time. CRN uses domain adversarial training to build balancing representations of the patient history. We show how our model achieves lower error in estimating counterfactuals and in choosing the correct treatment and timing of treatment.
  arXiv  Detail & Related papers  (2020-02-10T20:47:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.