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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 ($V^\pi = 9.48$) 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.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2510.08350 [cs.LG] |
| (or arXiv:2510.08350v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2510.08350 arXiv-issued DOI via DataCite |
From: Daniel Jason Tan [view email]
[v1]
Thu, 9 Oct 2025 15:37:56 UTC (896 KB)
[v2]
Wed, 19 Nov 2025 15:14:58 UTC (906 KB)
[v3]
Mon, 25 May 2026 16:01:35 UTC (764 KB)
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