Construction of a prognostic hierarchical model of intermediate-risk acute myeloid leukemia based on machine learning
An interpretable machine learning framework built from accessible clinical and genomic variables provided quantitative risk discrimination for IR-AML across development and external test cohorts, supporting individualized risk assessment and informing refinement of prognostic stratification.
Authors
- Ran Niu (ORCID: https://orcid.org/0000-0001-7791-3914)
- Bianhong Wang
- Ziqi Li (ORCID: https://orcid.org/0009-0007-2216-4953)
- Y. Yang (ORCID: https://orcid.org/0009-0002-3061-3652)
- Congmin Zhu (ORCID: https://orcid.org/0000-0002-0472-9128)
- Honglei Liu (ORCID: https://orcid.org/0000-0003-2963-5248)
Institutions
- Capital University (US)
- Capital Medical University (CN)
- Beijing Jiaotong University (CN)
- ShanghaiTech University (CN)
- Beijing Tsinghua Chang Gung Hospital (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251404525
- Primary Topic
- Acute Myeloid Leukemia Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00