Cargando…
Machine Learning to Identify Metabolic Subtypes of Obesity: A Multi-Center Study
BACKGROUND AND OBJECTIVE: Clinical characteristics of obesity are heterogenous, but current classification for diagnosis is simply based on BMI or metabolic healthiness. The purpose of this study was to use machine learning to explore a more precise classification of obesity subgroups towards inform...
Autores principales: | Lin, Ziwei, Feng, Wenhuan, Liu, Yanjun, Ma, Chiye, Arefan, Dooman, Zhou, Donglei, Cheng, Xiaoyun, Yu, Jiahui, Gao, Long, Du, Lei, You, Hui, Zhu, Jiangfan, Zhu, Dalong, Wu, Shandong, Qu, Shen |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8317220/ https://www.ncbi.nlm.nih.gov/pubmed/34335479 http://dx.doi.org/10.3389/fendo.2021.713592 |
Ejemplares similares
-
A multi-center study on glucometabolic response to bariatric surgery for different subtypes of obesity
por: Liu, Yao, et al.
Publicado: (2022) -
Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes
por: Arefan, Dooman, et al.
Publicado: (2021) -
A machine and human reader study on AI diagnosis model safety under attacks of adversarial images
por: Zhou, Qianwei, et al.
Publicado: (2021) -
Radiomics-Based Features for Prediction of Histological Subtypes in Central Lung Cancer
por: Li, Huanhuan, et al.
Publicado: (2021) -
Identifying Prognostic Markers From Clinical, Radiomics, and Deep Learning Imaging Features for Gastric Cancer Survival Prediction
por: Hao, Degan, et al.
Publicado: (2022)