Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms
This study employed interpretable machine learning to characterize risk factor profiles for multiple T2DM complications, identifying both common and distinct factors associated with major complications. The findings provide a foundation for exploring personalized risk management strategies and highlight the potential of data-driven approaches to inform early intervention research in T2DM complications.
Authors
- Louyan Ma
- Xiaomiao Li
- Zhancheng Gao (ORCID: https://orcid.org/0000-0002-8495-9221)
- Tianxiao Zhang (ORCID: https://orcid.org/0000-0002-8382-5934)
- Haochen Liu (ORCID: https://orcid.org/0000-0001-9794-5278)
- Yi Zhang (ORCID: https://orcid.org/0000-0001-7201-2092)
- Xinyu Li (ORCID: https://orcid.org/0000-0001-7203-7417)
- Lei Feng (ORCID: https://orcid.org/0009-0009-0129-2977)
- Shiyu Wang (ORCID: https://orcid.org/0000-0002-3237-5451)
- Ke Shi (ORCID: https://orcid.org/0000-0001-9149-9000)
- Ziyan Wang (ORCID: https://orcid.org/0000-0003-2017-1993)
- Yunxi Liu (ORCID: https://orcid.org/0009-0008-1320-800X)
- Jing Zhu (ORCID: https://orcid.org/0009-0003-0988-5191)
- Jiajia Zhai (ORCID: https://orcid.org/0000-0001-7697-9102)
- Yu Niu
Institutions
- Yanan University Affiliated Hospital (CN)
- Wuhan No.1 Hospital (CN)
- Xijing Hospital (CN)
- First Hospital of Xi'an (CN)
- Carnegie Mellon University (US)
- Xi'an Jiaotong University (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-09-10
- DOI
- https://doi.org/10.3389/fendo.2025.1657366
- Primary Topic
- Diabetic Foot Ulcer Assessment and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Basic Research Program of Shaanxi Province