Enhancing Glomerular Hematuria Identification in Automated Urinalysis Using a Light Gradient Boosting Machine-Based Model: A Diagnostic Accuracy Study
This interpretable LightGBM-based model offers a substantial improvement in automated GH identification and is a promising tool for classifying hematuria sources.
Authors
- Jingdi Zhang (ORCID: https://orcid.org/0009-0001-4411-8110)
- Peng Xia (ORCID: https://orcid.org/0000-0001-7771-5934)
- Z. Jennifer Huang (ORCID: https://orcid.org/0009-0005-9868-1397)
- Yongzhe Li (ORCID: https://orcid.org/0000-0002-8267-0985)
- Rongrong Wang (ORCID: https://orcid.org/0000-0002-3146-5228)
- Xu Jia (ORCID: https://orcid.org/0009-0000-1751-1761)
- Jing Jin (ORCID: https://orcid.org/0009-0004-6153-1558)
- Ye Guo (ORCID: https://orcid.org/0009-0006-2409-041X)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
Publication Details
- Journal
- Annals of Laboratory Medicine
- Published
- 2025-12-17
- DOI
- https://doi.org/10.3343/alm.2025.0394
- Primary Topic
- Chronic Kidney Disease and Diabetes
- Type
- article
- Field-Weighted Citation Impact
- 0.00