Machine Learning-Based Prediction of Malnutrition in Surgical In-Patients: A Validation Pilot Study

Abstract

Background: Malnutrition in hospitalised patients can lead to serious complications, worse patient outcomes and longer hospital stays. State-of-the-art screening methods rely on scores, which need additional manual assessments causing higher workload. Objectives: The aim of this prospective study was to validate a machine learning (ML)-based approach for an automated prediction of malnutrition in hospitalised patients. Methods: For 159 surgical in-patients, an assessment of malnutrition by dieticians was compared to the ML-based prediction conducted in the evening of admission. Results: The model achieved an accuracy of 83.0% and an AUROC of 0.833 in the prospective validation cohort. Conclusion: The results of this pilot study indicate that an automated malnutrition screening could replace manual screening tools in hospitals.