Development and validation of machine learning models for predicting cancer-related fatigue in lymphoma survivors.

Journal: International journal of medical informatics
PMID: 39293162

Abstract

BACKGROUND: New cases of lymphoma are rising, and the symptom burden, like cancer-related fatigue (CRF), severely impacts the quality of life of lymphoma survivors. However, clinical diagnosis and treatment of CRF are inadequate and require enhancement.

Authors

  • Yiming Wang
    Teaching Resource Information Service Center, Changchun Institute of Education, Changchun, China.
  • Lv Tian
    Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China.
  • Wenqiu Wang
    Department of Hematology, the Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266000, China.
  • Weiping Pang
    Department of Hematology, the Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266000, China.
  • Yue Song
    School of Science, Shenyang University of Technology, Shenyang 110870, China.
  • Xiaofang Xu
    Department of Hematology, the Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266000, China.
  • Fengzhi Sun
    Department of Hematology, the Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266000, China.
  • Wenbo Nie
    School of Nursing, Jilin University, No.965 Xinjiang Street, Changchun, 130021, China.
  • Xia Zhao
    Stony Brook University, Stony Brook, NY.
  • Lisheng Wang
    Department of Automation, Shanghai Jiaotong University, China.

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