Artikel ; Online: MUST-Plus: A Machine Learning Classifier That Improves Malnutrition Screening in Acute Care Facilities.
Journal of the American College of Nutrition
2020 Band 40, Heft 1, Seite(n) 3–12
Abstract: Objective: Malnutrition among hospital patients, a frequent, yet under-diagnosed problem is associated with adverse impact on patient outcome and health care costs. Development of highly accurate malnutrition screening tools is, therefore, essential for ...
Abstract | Objective: Malnutrition among hospital patients, a frequent, yet under-diagnosed problem is associated with adverse impact on patient outcome and health care costs. Development of highly accurate malnutrition screening tools is, therefore, essential for its timely detection, for providing nutritional care, and for addressing the concerns related to the suboptimal predictive value of the conventional screening tools, such as the Malnutrition Universal Screening Tool (MUST). We aimed to develop a machine learning (ML) based classifier (MUST-Plus) for more accurate prediction of malnutrition. Method: A retrospective cohort with inpatient data consisting of anthropometric, lab biochemistry, clinical data, and demographics from adult (≥ 18 years) admissions at a large tertiary health care system between January 2017 and July 2018 was used. The registered dietitian (RD) nutritional assessments were used as the gold standard outcome label. The cohort was randomly split (70:30) into training and test sets. A random forest model was trained using 10-fold cross-validation on training set, and its predictive performance on test set was compared to MUST. Results: In all, 13.3% of admissions were associated with malnutrition in the test cohort. MUST-Plus provided 73.07% (95% confidence interval [CI]: 69.61%-76.33%) sensitivity, 76.89% (95% CI: 75.64%-78.11%) specificity, and 83.5% (95% CI: 82.0%-85.0%) area under the receiver operating curve (AUC). Compared to classic MUST, MUST-Plus demonstrated 30% higher sensitivity, 6% higher specificity, and 17% increased AUC. Conclusions: ML-based MUST-Plus provided superior performance in identifying malnutrition compared to the classic MUST. The tool can be used for improving the operational efficiency of RDs by timely referrals of high-risk patients. |
---|---|
Mesh-Begriff(e) | Adult ; Humans ; Machine Learning ; Malnutrition/diagnosis ; Mass Screening ; Nutrition Assessment ; Retrospective Studies |
Sprache | Englisch |
Erscheinungsdatum | 2020-07-23 |
Erscheinungsland | United States |
Dokumenttyp | Journal Article ; Randomized Controlled Trial ; Research Support, N.I.H., Extramural |
ZDB-ID | 603204-7 |
ISSN | 1541-1087 ; 0731-5724 |
ISSN (online) | 1541-1087 |
ISSN | 0731-5724 |
DOI | 10.1080/07315724.2020.1774821 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
Volltext online
Zusatzmaterialien
Kategorien
Verfügbar in ZB MED Köln/Königswinter
Zs.A 1759: Hefte anzeigen | Standort: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG) |
Über subito bestellen
Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.