arXiv:2607. 26521v1 Announce Type: new Abstract: Conventional subgroup analyses can yield unstable and difficult-to-interpret conclusions, especially in observational biomedical data where each individual is observed under only one exposure state, true individual treatment effects are unavailable, and causal structure is uncertain.
By Vasundhara Acharya, Bulent Yener
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.
By Saman Khamesian, Hassan Ghasemzadeh
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.
By Nafiseh Payani, Soham Das, G. Anthony Wilson, Anahita Khojandi
arXiv:2606. 31036v1 Announce Type: new Abstract: Specialist epilepsy expertise is scarce in resource-constrained settings, making LLM-based decision support attractive for frontline clinicians managing longitudinal treatment.
By Shreyas Rajesh, Kartik Sharma, Tonmoy Monsoor, Mehmet Yigit Turali, Richard Idro, Juliana Kayaga, Robert Sebunya, Tracy Tushabe Namata, Jessica Nichole Pasqua, Vwani Roychowdhury, Rajarshi Mazumder
arXiv:2508. 03875v2 Announce Type: replace Abstract: Many sequential decision problems offer qualitatively different ways of influencing the environment: some interventions act immediately, whereas others induce persistent effects that continue to shape future states long after the decision that initiated them.
By David Mguni, Wanrong Yang, Jing Dong, Ziquan Liu, Muhammad Salman Haleem, Baoxiang Wang, Dominik Wojtczak
The paper introduces PAFIR, a Personalized and Adaptive Feature selection framework that treats feature selection as a reinforcement learning problem over longitudinal multimodal health data. PAFIR jointly models structural dependencies among assessment variables and temporal dynamics in wearable-derived physical activity data, learning adaptive selection policies across repeated study visits using reward signals from sparse fall incidence outcomes. Applied to the Physio Feedback Exercise Program (PEER) trial, PAFIR outperforms state‑of‑the‑art baselines by capturing longitudinal and structural patterns of feature relevance, enabling dynamic, subject‑specific feature selection for more timely fall prevention strategies.
By Chang Liu, Ladda Thiamwong, Yanjie Fu, Rui Xie