mlsanityData to 14 Oct 2026

DeepEN: A Deep Reinforcement Learning Framework for Personalized Enteral Nutrition in Critical Care

arXiv 2510.08350 ↗First posted 9 Oct 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

Objective: Enteral nutrition (EN) delivery in the ICU remains suboptimal due to limited personalization and uncertainty regarding appropriate calorie, protein, and fluid targets under dynamic metabolic demands. We introduce DeepEN, a reinforcement learning (RL) framework for personalized EN optimization using electronic health record data. Methods: DeepEN was trained on over 11,000 ICU patients from MIMIC-IV to generate 4-hourly, patient-specific caloric, protein, and fluid targets. The state representation incorporated demographics, comorbidities, vital signs, laboratory values, and recent interventions. A physiologically aligned reward framework balanced biomarker stability with long-term survival. Policy learning employed a dueling double deep Q-network with Conservative Q-Learning regularization to enable safe offline training. Results: DeepEN achieved the highest estimated policy value (Vπ = 9.48) and the lowest calibrated mortality (18.8 +/- 1.0%), representing a 4.0 percentage-point absolute reduction compared with clinician practice (22.8%). The policy also demonstrated superior metabolic stability, achieving the highest proportion of glucose, phosphate, and sodium values within target range. Furthermore, deviation from the DeepEN policy was independently associated with increased mortality and biomarker instability, whereas deviation from a random policy showed no such association. Interpretability analyses further indicated that recommendations were conditioned on physiologically relevant markers of organ function and metabolic status rather than static dosing heuristics. Conclusion: DeepEN demonstrates the feasibility of conservative offline RL for safe, individualized EN optimization, highlighting the potential of data-driven personalization to complement guideline-based approaches in critical care.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 63 references that are in the map. First posted on arXiv 9 Oct 2025.

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15 of 63 references.

References · 15

  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  2. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,809
  3. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  4. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 786
  5. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  6. 2015Doubly Robust Off-policy Value Evaluation for Reinforcement Learningcited by 339
  7. 2019Reinforcement Learning in Healthcare: A Surveycited by 310
  8. 2016Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learningcited by 286
  9. 2019Batch Policy Learning under Constraintscited by 244
  10. 2017Deep Reinforcement Learning for Sepsis Treatmentcited by 71
  11. 2017Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approachcited by 68
  12. 2018Evaluating Reinforcement Learning Algorithms in Observational Health Settingscited by 60
3 more
  1. 2017Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patientscited by 13
  2. 2022A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategiescited by 5
  3. 2024Advancing Multi-Organ Disease Care: A Hierarchical Multi-Agent Reinforcement Learning Frameworkcited by 4

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