Disease classification via interpretable machine learning based on multi-center routine coagulation test
Machine learning models based on multi-center coagulation tests provide effective and interpretable disease classification, supporting clinical diagnostic automation.
Authors
- W.H. Chen
- Dong Feng (ORCID: https://orcid.org/0000-0002-7612-8823)
- Chuanbao Li
- C. Wang
- Xi Zhang (ORCID: https://orcid.org/0000-0002-4638-3997)
- Lei Zhang (ORCID: https://orcid.org/0000-0002-2227-0613)
- Yutong Hou
- Guobin Xu
- Ruichuang Yang
- Jun Wu
- Xiaoling Gao
- Jiandang Ma
- Yaqiong Zhang
- Minghua Jiang
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking University (CN)
- Beijing Jiaotong University (CN)
- Wenzhou Medical University (CN)
- Beijing Jishuitan Hospital (CN)
- ShenZhen People’s Hospital (CN)
- People's Hospital of Xinjiang Uygur Autonomous Region (CN)
- Luoyang Central Hospital Affiliated to Zhengzhou University (CN)
- Hainan General Hospital (CN)
- Second Affiliated Hospital of Xi'an Jiaotong University (CN)
- Taizhou Central Hospital (CN)
- Ministry of Education (ET)
- Mindray (China) (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Molecular Biosciences
- Published
- 2026-03-04
- DOI
- https://doi.org/10.3389/fmolb.2026.1788536
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00