Director of Department of Food and Nutrition
Taichung Veterans General Hospital, Taiwan (Republic of China)
Criticaliy ill patients are at high nutritional risk with higher mortality rates and require immediate nutritional support. The predominant method of nutritional risk assessment, mNUTRIC score, lacks information on the patients' body weight and feeding. This study established a predictive nutrition risk machine model using artificial intelligence and data integration to increase the accuracy in assessing nutritional risk of criticaliy ill patients. Enable real-time nutritional interventions for high-risk populations.