ICU enteral feeding remains sub-optimal due to limited personalization and uncertainty about appropriate calorie, protein, and fluid targets, particularly under rapidly changing metabolic demands and heterogeneous patient responses. This study introduces DeepEN, a reinforcement learning (RL)-based framework that personalizes enteral nutrition (EN) dosing for critically ill patients using electronic health record data. DeepEN was trained on over 11,000 ICU patients from the MIMIC-IV database to generate 4-hourly, patient-specific targets for caloric, protein, and fluid intake. The model's state space integrates demographics, comorbidities, vital signs, laboratory results, and prior interventions relevant to nutritional management, while its reward function balances short-term physiological and nutrition-related goals with long-term survival. A dueling double deep Q-network with Conservative Q-Learning regularization is used to ensure safe and reliable policy learning from retrospective data. DeepEN achieved a 3.7 $\pm$ 0.17 percentage-point absolute reduction in estimated mortality compared with the clinician policy (18.8% vs 22.5%) and higher expected returns compared with guideline-based dosing (11.89 vs 8.11), with improvements in key nutritional biomarkers. U-shaped associations between deviations from clinician dosing and mortality suggest that the learned policy aligns with high-value clinician actions while diverging from suboptimal ones. These findings demonstrate the feasibility of conservative offline RL for individualized EN therapy and suggest that data-driven personalization may improve outcomes beyond guideline- or heuristic-based approaches.

翻译：重症监护室（ICU）的肠内喂养因个性化程度有限以及热量、蛋白质和液体目标的不确定性而仍处于次优状态，尤其是在代谢需求快速变化和患者反应异质性的情况下。本研究提出DeepEN，一种基于强化学习（RL）的框架，利用电子健康记录数据为危重患者个性化制定肠内营养（EN）剂量。DeepEN在MIMIC-IV数据库中超过11,000名ICU患者的数据上进行训练，以生成每4小时一次、针对患者个体的热量、蛋白质和液体摄入目标。该模型的状态空间整合了人口统计学特征、合并症、生命体征、实验室结果以及与营养管理相关的既往干预措施，而其奖励函数则平衡了短期生理和营养相关目标与长期生存率。采用带保守Q学习正则化的决斗双深度Q网络，以确保从回顾性数据中进行安全可靠的政策学习。与临床医生政策相比，DeepEN实现了估计死亡率绝对降低3.7±0.17个百分点（18.8%对比22.5%），与基于指南的剂量相比获得了更高的预期回报（11.89对比8.11），并在关键营养生物标志物方面有所改善。偏离临床医生剂量与死亡率之间的U型关联表明，学习到的政策与高价值的临床医生行为保持一致，同时避免了次优行为。这些发现证明了保守离线强化学习在个体化EN治疗中的可行性，并表明数据驱动的个性化可能超越基于指南或启发式的方法，改善临床结局。