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arXiv Machine Learning By Nafiseh Payani, Soham Das, G. Anthony Wilson, Anahita Khojandi

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

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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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