多维预测因癌症而导致的疲劳,基于预定,沉和延续 (3P) 模型:系统性审查
Yiming Wang1, Lv Tian1, Xia Liu2
1School of Nursing, Jilin University, No. 965 Xinjiang Street, Changchun 130021, China.
Cancers
|December 23, 2023
概括
与癌症相关的疲劳 (CRF) 显著影响癌症患者.
科学领域:
- 在瘤学瘤学.
- 精神瘤学 精神瘤学
- 计算医学是一种计算医学.
背景情况:
- 与癌症相关的疲劳 (CRF) 是影响癌症患者生活质量的普遍和衰弱症状.
- 精确的护理需要早期查和评估CRF的工具.
- 机器学习模型为准确的CRF预测提供了潜力.
研究的目的:
- 确定癌症相关疲劳 (CRF) 的关键预测因子,用于机器学习模型开发.
- 使用3P因子模型系统地审查和分类CRF预测器.
主要方法:
- 在PubMed,Web of Science,Embase和Scopus进行了全面的文献搜索.
- 分析了27项符合纳入标准的研究.
- 系统地评估了CRF的诱发性,沉性和延续性 (3P) 因素.
主要成果:
- 诱导因素包括基线疲劳,人口统计,临床特征,心理社会特征和身体症状.
- 导致的因素包括化疗细节,炎症标志物,实验室指标和代谢变化.
- 延续因素包括体力活动不足和营养状况不佳.
结论:
- 准确的CRF预测器对于开发有效的基于机器学习的预测模型至关重要.
- 未来的研究应该专注于大规模的前性研究和新兴技术.
- 在CRF评估和管理中针对可控制的诱发因素可以改善幸存者的生活质量.
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