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EXIST: EXamining rIsk of excesS adiposiTy—Machine learning to predict obesity‐related complications

Authors :
Alexander Turchin
Fritha J. Morrison
Maria Shubina
Ilya Lipkovich
Shraddha Shinde
Nadia N. Ahmad
Hong Kan
Source :
Obesity Science & Practice, Vol 10, Iss 1, Pp n/a-n/a (2024)
Publication Year :
2024
Publisher :
Wiley, 2024.

Abstract

Abstract Background Obesity is associated with an increased risk of multiple conditions, ranging from heart disease to cancer. However, there are few predictive models for these outcomes that have been developed specifically for people with overweight/obesity. Objective To develop predictive models for obesity‐related complications in patients with overweight and obesity. Methods Electronic health record data of adults with body mass index 25–80 kg/m2 treated in primary care practices between 2000 and 2019 were utilized to develop and evaluate predictive models for nine long‐term clinical outcomes using a) Lasso‐Cox models and b) a machine‐learning method random survival forests (RSF). Models were trained on a training dataset and evaluated on a test dataset over 100 replicates. Parsimonious models of

Details

Language :
English
ISSN :
20552238 and 87205742
Volume :
10
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Obesity Science & Practice
Publication Type :
Academic Journal
Accession number :
edsdoj.05d5ee872057420c835dd48e43486699
Document Type :
article
Full Text :
https://doi.org/10.1002/osp4.707