Outpatient acute kidney disease detection by national and institutional health data
By integrating the national-local data using the AKIDS, this study comprehensively characterizes a previously unrecognized phenotype, AKD<sub>OPT</sub>, illustrating the potential of healthcare big data in transforming global approaches to AKD patterns and prevention.
Authors
- Yun-Lun Chang
- Chin‐Chi Kuo (ORCID: https://orcid.org/0000-0002-2050-1377)
- Bradley Chen (ORCID: https://orcid.org/0000-0001-6024-673X)
- Jie-Sian Wang (ORCID: https://orcid.org/0000-0002-1943-3709)
- Min-Huang Hsieh
- Zi-Han Lin (ORCID: https://orcid.org/0000-0003-0137-7646)
- Hsiu‐Yin Chiang (ORCID: https://orcid.org/0000-0002-4205-2787)
- Hung-Lin Chen (ORCID: https://orcid.org/0000-0002-6825-3691)
- Tsang‐Bin Tzeng
- Yu‐Chih Liu
- I‐Wen Ting
- Hung‐Chieh Yeh
- David R. Chang (ORCID: https://orcid.org/0000-0002-2213-5067)
Institutions
- Harvard University (US)
- China Medical University (TW)
- China Medical University Hospital (TW)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1038/s43856-025-00836-4
- Citations
- 1
- Primary Topic
- Chronic Kidney Disease and Diabetes
- Type
- article
- Field-Weighted Citation Impact
- 1.43
Funders
- National Science and Technology Council