  Background and Objectives: We aim to establish deep learning models to
optimize the individualized energy delivery for septic patients. Methods and
Study Design: We conducted a study of adult septic patients in Intensive Care
Unit (ICU), collecting 47 indicators for 14 days. After data cleaning and
preprocessing, we used stats to explore energy delivery in deceased and
surviving patients. We filtered out nutrition-related features and divided the
data into three metabolic phases: acute early, acute late, and rehabilitation.
Models were built using data before September 2020 and validated on the rest.
We then established optimal energy target models for each phase using deep
learning. Results: A total of 277 patients and 3115 data were included in this
study. The models indicated that the optimal energy targets in the three phases
were 900kcal/d, 2300kcal/d, and 2000kcal/d, respectively. Excessive energy
intake increased mortality rapidly in the early period of the acute phase.
Insufficient energy in the late period of the acute phase significantly raised
the mortality of septic patients. For the rehabilitation phase, too much or too
little energy delivery both associated with high mortality. Conclusion: Our
study established time-series prediction models for septic patients to optimize
energy delivery in the ICU. This approach indicated the feasibility of
developing nutritional tools for critically ill patients. We recommended
permissive underfeeding only in the early acute phase. Later, increased energy
intake may improve survival and settle energy debts caused by underfeeding.
