Factors to improve distress and fatigue in Cancer survivorship; further understanding through text analysis of interviews by machine learning.

Journal: BMC cancer
PMID: 34176470

Abstract

BACKGROUND: From patient-reported surveys and individual interviews by health care providers, we attempted to identify the significant factors related to the improvement of distress and fatigue for cancer survivors by text analysis with machine learning techniques, as the secondary analysis using the single institute data from the Korean Cancer Survivorship Center Pilot Project.

Authors

  • Kyungmi Yang
    Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
  • Jina Kim
    Department of Interaction Science, Sungkyunkwan University, Seoul, 03063, Republic of Korea.
  • Mison Chun
    Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea. chunm@ajou.ac.kr.
  • Mi Sun Ahn
    Departments of Hematology-Oncology.
  • Eunae Chon
    Cancer Survivorship Center, Ajou University School of Medicine, Suwon, Republic of Korea.
  • Jinju Park
    Cancer Survivorship Center, Ajou University School of Medicine, Suwon, Republic of Korea.
  • Mijin Jung
    Cancer Survivorship Center, Ajou University School of Medicine, Suwon, Republic of Korea.

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