A machine-learned model for predicting weight loss success using weight change features early in treatment
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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A machine-learned model for predicting weight loss success using weight change features early in treatment ; volume:7 ; number:1 ; day:29 ; month:11 ; year:2024 ; pages:1-10 ; date:12.2024
npj digital medicine ; 7, Heft 1 (29.11.2024), 1-10, 12.2024
- Creator
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Shahabi, Farzad
Battalio, Samuel L.
Pfammatter, Angela Fidler
Hedeker, Donald
Spring, Bonnie
Alshurafa, Nabil
- Contributor
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SpringerLink (Online service)
- DOI
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10.1038/s41746-024-01299-y
- URN
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urn:nbn:de:101:1-2502142111193.581700520695
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:38 AM CEST
Data provider
This object is provided by:
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Shahabi, Farzad
- Battalio, Samuel L.
- Pfammatter, Angela Fidler
- Hedeker, Donald
- Spring, Bonnie
- Alshurafa, Nabil
- SpringerLink (Online service)
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Predicting weight loss and maintenance in overweight/obese pediatric patients
Development and validation of a weight-loss predictor to assist weight loss management
Association of weight and shape concern with weight change and weight-related behaviors in behavioral weight loss treatment
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Weight loss and weight gain among participants in a community-based weight loss Challenge
Gut microbiota patterns predicting long-term weight loss success in individuals with obesity undergoing nonsurgical therapy
Predicting the Risk of Weight Loss After Esophageal Cancer Surgery
Predicting weight loss and maintenance in overweight/obese pediatric patients
Development and validation of a weight-loss predictor to assist weight loss management
Association of weight and shape concern with weight change and weight-related behaviors in behavioral weight loss treatment
Equation for Predicting Weight Gain in Very Low-Birth-Weight Infants*
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