Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
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SHR Neuro Krebs Kardio Lipid Stoffw Microb
Kramer, D; Jauk, S; Veeranki, S; Schrempf, M; Traub, J; Kugel, E; Prisching, A; Domnanich, S; Leopold, M; Krisper, P; Sendlhofer, G.
Machine Learning-Based Prediction of Malnutrition in Surgical In-Patients: A Validation Pilot Study.
Stud Health Technol Inform. 2024; 313: 156-157.
Doi: 10.3233/SHTI240029
PubMed
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- Co-Autor*innen der Med Uni Graz
- Krisper Peter
- Schrempf Michael
- Sendlhofer Gerald
- Traub Julia
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- 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.
- Find related publications in this database (using NLM MeSH Indexing)
- Humans - administration & dosage
- Machine Learning - administration & dosage
- Pilot Projects - administration & dosage
- Malnutrition - diagnosis
- Male - administration & dosage
- Female - administration & dosage
- Prospective Studies - administration & dosage
- Aged - administration & dosage
- Middle Aged - administration & dosage
- Nutrition Assessment - administration & dosage