开发一种基于机器学习算法的预测模型,用于肺癌幸存者的身体活动水平:一项跨部门研究
Qiaoqiao Ma1, Rui Wang1, Mengyan Mo2
1Department of Nursing, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China.
Journal of clinical nursing
|July 8, 2025
概括
一个机器学习模型准确地预测了肺癌幸存者的身体活动水平. 该工具有助于识别需要个性化康复的个人,以改善他们的生活质量.
科学领域:
- 在瘤学瘤学.
- 康复医学 康复医学 康复医学
- 数据科学数据科学数据科学
背景情况:
- 肺癌幸存者经常表现出低水平的体力活动,影响他们的生活质量.
- 身体活动是癌症康复的关键非药物干预.
- 识别影响身体活动的因素是有针对性的干预措施的关键.
研究的目的:
- 为了研究肺癌幸存者的身体活动水平.
- 分析影响这些活动水平的因素.
- 开发基于机器学习的身体活动预测模型.
主要方法:
- 一项横截面研究调查了中国14家医院的2231名肺癌幸存者.
- 收集了人口统计,疾病,健康,身体和心理社会因素的数据.
- 评估了四种机器学习模型,其中随机森林因其卓越的性能而被选中 (AUC-ROC 0.86).
主要成果:
- 30%的幸存者身体活动水平较低.
- 确定了15个独立因素,包括握力,MDASI得分和抑郁症得分.
- 一个随机森林模型实现了0.86的AUC-ROC,从而产生了一个在线预测工具.
结论:
- 一个机器学习模型,特别是随机森林,准确地预测肺癌幸存者的身体活动.
- 开发的工具有助于早期识别低活动幸存者.
- 促进及时,个性化的康复和健康管理,以改善生活质量.
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