A potential predictive model based on machine learning and CPD parameters in elderly patients with aplastic anemia and myelodysplastic neoplasms.

Journal: BMC medical informatics and decision making
PMID: 39695587

Abstract

BACKGROUND: Aplastic anemia (AA) and myelodysplastic neoplasms (MDS) have similar peripheral blood manifestations and are clinically characterized by reduced hematological triad. It is challenging to distinguish and diagnose these two diseases. Hence, utilizing machine learning methods, we employed and validated an algorithm that used cell population data (CPD) parameters to diagnose AA and MDS.

Authors

  • Yuxiang Qi
    School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
  • Xu Liu
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore. liuxu16@bjut.edu.cn.
  • Zhishan Ding
    School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
  • Ying Yu
    School of Chemistry and Environment, Guangzhou Key Laboratory of Analytical Chemistry for Biomedicine, South China Normal University, Guangzhou 510006, PR China. Electronic address: yuyhs@scnu.edu.cn.
  • Zhenchao Zhuang
    Adicon Clinical Laboratories, Hangzhou, 310023, Zhejiang, China. zhuangzzc2015@163.com.

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