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

作者：Kyungmi Yang, Jina Kim, Mison Chun, Mi Sun Ahn, Eunae Chon, Jinju Park, Mijin Jung · 发表于：BMC Cancer · 年份：2021 · DOI：10.1186/s12885-021-08438-8 · 被引用次数：6 · 研究领域：Cancer survivorship and care、Health and Wellbeing Research、Pain Management and Opioid Use

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. METHODS: Surveys and in-depth interviews from 322 cancer survivors were analyzed to identify their needs and concerns. Among the keywords in the surveys, including EQ-VAS, distress, fatigue, pain, insomnia, anxiety, and depression, distress and fatigue were focused. The interview transcripts were analyzed via Korean-based text analysis with machine learning techniques, based on the keywords used in the survey. Words were generated as vectors and similarity scores were calculated by the distance related to the text's keywords and frequency. The keywords and selected high-ranked ten words for each keyword based on the similarity were then taken to draw a network map. RESULTS: Most participants were otherwise healthy females younger than 50 years suffering breast cancer who completed treatment less than 6 months ago. As the 1-month follow-up survey's results, the improved patients were 56.5 and 58.4% in distress and fatigue scores, respectively. For the improvement of distress, dyspepsia (p = 0.006) and initial scores of distress, fatigue, anxiety, and depression (p < 0.001, < 0.001, 0.043, and 0.013, re...