Objective: We propose an automated nutritional assessment algorithm that provides a method for malnutrition risk prediction with high accuracy and reliability.
Methods: The database used for this study was a file of 432 patients, where each patient was described by 4 laboratory parameters and 11 clinical parameters. A malnutrition risk assessment of low (1), moderate (2), or high (3) was assigned by a dietitian for each patient.