Machine Learning-Based Prediction of Malnutrition in Surgical In-Patients: A Validation Pilot Study.
Stud Health Technol Inform
; 313: 156-157, 2024 Apr 26.
Article
en En
| MEDLINE
| ID: mdl-38682522
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.Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Desnutrición
/
Aprendizaje Automático
Idioma:
En
Revista:
Stud Health Technol Inform
Asunto de la revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Año:
2024
Tipo del documento:
Article