Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis.
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| Title: | Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis. |
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| Authors: | Li Y; Department of Geriatrics, Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China., Jin N; Department of Geriatrics, Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China., Zhan Q; Faculty of Chinese Medicine, Macau University of Science and Technology, Macao, Macao SAR, China., Huang Y; Department of Geriatrics, Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China., Sun A; Department of Geriatrics, Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China.; Graduate School of Beijing University of Chinese Medicine, Beijing, China., Yin F; Department of Geriatrics, Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China.; Graduate School of Beijing University of Chinese Medicine, Beijing, China., Li Z; Department of Geriatrics, Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China., Hu J; Department of Geriatrics, Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China., Liu Z; Department of Geriatrics, Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China. |
| Source: | Frontiers in endocrinology [Front Endocrinol (Lausanne)] 2025 Mar 03; Vol. 16, pp. 1495306. Date of Electronic Publication: 2025 Mar 03 (Print Publication: 2025). |
| Publication Type: | Journal Article; Systematic Review; Meta-Analysis |
| Journal Info: | Publisher: Frontiers Research Foundation] Country of Publication: Switzerland NLM ID: 101555782 Publication Model: eCollection Cited Medium: Print ISSN: 1664-2392 (Print) Linking ISSN: 16642392 NLM ISO Abbreviation: Front Endocrinol (Lausanne) Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1664-2392 |
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| DOI: | 10.3389/fendo.2025.1495306 |
