Deep Phenotyping of Obesity: Electronic Health Record–Based Temporal Modeling Study
In this proof-of-concept study, we demonstrated longitudinal EHR as a valuable resource for deep phenotyping the pre-AOM period at per patient visit level. Our analysis revealed the presence of clusters with distinct clinical significance, which could have implications in AOM treatment options. Further research using larger, independent cohorts is necessary to validate the reproducibility and clinical relevance of these clusters, uncover more detailed substructures and corresponding AOM treatment responses.
Authors
- Andrew Wen (ORCID: https://orcid.org/0000-0001-9090-8028)
- Shuyu Lu (ORCID: https://orcid.org/0000-0001-8486-2246)
- Xiaoyang Ruan (ORCID: https://orcid.org/0009-0006-7085-744X)
- Liwei Wang (ORCID: https://orcid.org/0000-0001-9970-8604)
- Sameer B. Murali (ORCID: https://orcid.org/0000-0003-2481-9910)
- Hongfang Liu (ORCID: https://orcid.org/0000-0003-2570-3741)
Institutions
- The University of Texas Health Science Center (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-05-08
- DOI
- https://doi.org/10.2196/70140
- Citations
- 1
- Primary Topic
- Diet and metabolism studies
- Type
- article
- Field-Weighted Citation Impact
- 1.16