Accessibility settings

This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (3449) Clinical Informatics (2098) Electronic Health Records (1631) Ontologies, Classifications, and Coding (403) Digital Biomarkers and Digital Phenotyping (407) Precision Medicine (414) Obesity (135) Incidence and Prevalence of Obesity (13)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/70140, first published .
Child's belly measured with tape measure, highlighting childhood obesity concerns.

Deep Phenotyping of Obesity: Electronic Health Record–Based Temporal Modeling Study

Deep Phenotyping of Obesity: Electronic Health Record–Based Temporal Modeling Study

Authors of this article:

Xiaoyang Ruan1 Author Orcid Image ;   Shuyu Lu1 Author Orcid Image ;   Liwei Wang2 Author Orcid Image ;   Andrew Wen1 Author Orcid Image ;   Sameer Murali3 Author Orcid Image ;   Hongfang Liu1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Mehmood I, Zahra Z, Iqbal S, Qahmash A, Hussain I. A Systematic Review of Topic Modeling Techniques for Electronic Health Records. Healthcare 2026;14(2):282 View
  2. Wójcik S, Tomaszewska M, Rulkiewicz A. Artificial Intelligence-Based Risk Stratification in Obesity Care: From Diagnosis to Personalised Treatment Pathways. Diagnostics 2026;16(10):1461 View
  3. Zhao S, Cheang C, Lin J, Mio W, Che S, Kuok K. A multimodal, risk-stratified framework for AI-driven early risk prediction and personalised prevention in obesity. Frontiers in Artificial Intelligence 2026;9 View