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arXiv Machine Learning

Peer-Grounded Counterfactual Path Planning for Chronic Health Management

The paper introduces POROS, a framework that uses peer‑grounded counterfactual explanations to generate incremental behavioral steps for chronic disease management. POROS builds a directed acyclic graph of patient states where each edge represents a behavior change that peers have successfully achieved and that improves health outcomes. In two diabetes cohorts, POROS reduces the required improvement per step from over 25–30 percentage points to about 5–6, while most multi‑hop paths involve cross‑patient comparisons.

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Patient-Centered Treatment Planning for Chronic Multimorbidity: A Hierarchical Reinforcement Learning Framework for Preference Modeling

The paper introduces FAHOC, a hierarchical reinforcement learning framework that models patient preferences by learning high‑level therapeutic options and factored intra‑option policies, while enforcing a cooperation‑aware action masking mechanism. It demonstrates that cooperative patients achieve better health outcomes and that the Q‑function approximation error is bounded. Evaluated on data from ~50,000 comorbid hypertension and type 2 diabetes patients, FAHOC improves quality‑adjusted life years by 0.669, correctly identifies cooperative patients 95.9% of the time, and never violates patient preferences in held‑out tests.

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GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits

arXiv:2608. 02877v1 Announce Type: new Abstract: Causal discovery recovers directed structure from observational data and is increasingly used in clinical settings to support mechanism reasoning and fairness audits of predictive models.

By Nitish Nagesh, Elahe Khatibi, Thomas Dean Hughes, Mahdi Bagheri, Pratik Gajane, Amir M. Rahmani
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arXiv Machine Learning
Sep 16

Adaptive Bayesian Partner Selection for Federated Clinical Centers

Adaptive Bayesian Partner Selection (ABPS) is a peer‑to‑peer federated learning framework designed for heterogeneous clinical centers, where each center maintains a Beta‑Bernoulli posterior over prospective peers’ Shapley marginal utility and selects partners using an Upper Confidence Bound criterion. The lightweight propose‑reject mechanism allows centers to collaborate only when mutually beneficial, with the option to abstain from communication entirely. In experiments on 230 non‑IID ICU centers predicting in‑hospital mortality, the ABPS‑X variant achieves comparable accuracy to the strongest baseline (FedDyn) while reducing communication cost by 90% and enabling intentional isolation for many centers.

By Navid Seidi, Satyaki Roy, Sajal K. Das
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arXiv Machine Learning
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A Diffusion-Model Subpopulation Digital Twin for Mobile Health Deployment: A Case Study on the HeartSteps Intervention

arXiv:2607. 21403v1 Announce Type: new Abstract: Mobile-health interventions increasingly use online learning and decision making algorithms to personalize when to nudge users toward healthier behavior, but a poorly designed algorithm can burden and disengage participants.

By Ziping Xu, Yuyi Chang, Chenshun Ni, Nithin Sugavanam, Asim H. Gazi, Pedja Klasnja, Emre Ertin, Susan A. Murphy
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