Machine Learning-Based Prediction of Malnutrition in Surgical In-Patients: A Validation Pilot Study.
Studies in health technology and informatics(2024)
摘要
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.
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