A complete blood count-based machine learning model for rapid differentiation of aplastic anemia, immune thrombocytopenia, and myelodysplastic syndromes in routine clinical practice
A machine learning model based on routinely available CBC parameters can support non-invasive differentiation of common hematologic disorders. This approach may serve as a practical screening and triage tool at the outpatient or pre-bone marrow stage, helping optimize the use of invasive diagnostic procedures.
Authors
- Yu Yuan (ORCID: https://orcid.org/0000-0001-6932-6920)
- Wei Wang (ORCID: https://orcid.org/0000-0001-5161-8175)
- Jian An Huang
- Huan Liu
- Jingwen Xi
Institutions
- Sun Yat-sen University (CN)
- Guangdong Medical College (CN)
- Guangdong Pharmaceutical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Sixth Affiliated Hospital of Sun Yat-sen University (CN)
- First Affiliated Hospital of Guangdong Pharmaceutical University (CN)
- Institute of Hematology & Blood Diseases Hospital (CN)
- State Key Laboratory of Respiratory Disease (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Practical Laboratory Medicine
- Published
- 2026-03-12
- DOI
- https://doi.org/10.1016/j.plabm.2026.e00526
- Primary Topic
- Hematopoietic Stem Cell Transplantation
- Type
- article
- Field-Weighted Citation Impact
- 0.00