DeepEN: A Deep Reinforcement Learning Framework for Personalized Enteral Nutrition in Critical Care
arXiv:2510.08350v3 Announce Type: replace Abstract: 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, comorbid
The proliferation of clinical data and advancements in reinforcement learning are enabling the development of personalized AI-driven healthcare solutions.
This development indicates a tangible use case for AI in critical medical decision-making, potentially leading to improved patient outcomes and resource efficiency in healthcare.
Personalized nutritional plans in critical care could transition from static protocols to dynamic, AI-optimized interventions based on continuous patient data.
- · Hospitals and ICU departments
- · Patients in critical care
- · AI healthcare solution providers
- · Medical device manufacturers
- · Developers of generic nutritional guidelines
- · Traditional healthcare information systems
Improved patient recovery rates and reduced complications in ICUs due to optimized nutrition.
Increased demand for granular, real-time patient data collection and integration within hospital systems.
Ethical and regulatory frameworks will evolve to govern autonomous AI decision-making in critical medical interventions.
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Read at arXiv cs.LG