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Machine Learning-Based Prediction of Malnutrition in Surgical In-Patients: A Validation Pilot Study
Diether Kramer, Stefanie Jauk, Sai Veeranki, Michael Schrempf, Julia Traub, Eva Kugel, Anna Prisching, Sandra Domnanich, Maria Leopold, Peter Krisper, Gerald Sendlhofer
156 - 157
10.3233/SHTI240029
Research Article
Studies in Health Technology and Informatics
Volume 313: dHealth 2024
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.

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