Delphi: A Neuro-Symbolic Framework for Individualized, Safe and Interpretable Treatment Recommendation

Abstract Clinical reinforcement learning (RL) holds promise for treatment recommendation but remains hindered by black-box decision processes, limited safety guarantees, and lack of individualized reasoning. We introduce Delphi Engine, the first fully trainable neuro-symbolic causal RL framework for dynamic treatment planning, designed to answer three core clinical questions in real time: Why this action? Why is it safe? Why for this patient? Specifically, Delphi integrates: (1) causality-aware state modeling using discretized physiological variables and subtype-specific causal graphs; (2) adaptive symbolic rule constraints, combining clinical guidelines and behavior-derived rules into soft differentiable logic; and (3) interpretable decision fusion, where actions are selected based on joint neural-symbolic Q-values and explained via structured LLM-based justifications. We evaluate Delphi on the MIMIC-III sepsis cohort using both standard off-policy evaluations (WIS↑1.47, DR↑1.29, RMSE↓0.207) and the first blinded physician evaluation of an explainable RL system in healthcare. Delphi consistently outperforms historical physicians' treatments in safety (+10.4%), understandability (+8.9%), and adoption rate (+5.75%) across six clinical axes. These results highlight Delphi’s potential as a safe, interpretable, and patient-specific AI assistant for critical care medicine.