应用机器学习来构建肺癌和环境激素高风险因素的关联模型,以及护理评估重建
Pin-Chieh Lee1, Mong-Wei Lin2, Hsien-Chi Liao3
1Department of Nursing, National Taiwan University Cancer Center, Taipei, Taiwan.
概括
机器学习发现了肺癌与和酸盐等环境激素之间的联系. 这项研究旨在通过结合这些新型因素来改善肺癌风险评估.
科学领域:
- 环境健康 环境健康
- 在瘤学瘤学.
- 数据科学数据科学数据科学
背景情况:
- 肺癌是导致死亡的主要原因,许多病例与环境荷尔蒙等非传统风险因素有关.
- 目前的肺癌护理评估没有充分解决环境激素暴露的问题.
- 环境激素越来越多地被认为是各种疾病的潜在贡献者,包括癌症.
研究的目的:
- 开发一种基于机器学习的关联模型,将肺癌与环境荷尔蒙联系起来.
- 为了提高对肺癌风险因素的了解.
- 改进目前对肺癌风险的护理评估.
主要方法:
- 使用机器学习 (逻辑回归,神经网络) 对生物数据库数据的探索性研究.
- 第一个阶段:建模,以确定肺癌与环境暴露之间的关联.
- 第二阶段:对肺腺癌患者的模型验证,使用关于习惯和激素暴露的问卷.
主要成果:
- 肺癌与血液中的,尿中的,尿中的/肌,以及二-2-乙烯) 甲酸盐之间发现了显著的关联.
- 肺腺癌患者的习惯和暴露于,多芳香碳化合物,甲基甲酸盐和二甲基甲酸盐之间的相关性被确定.
- 这项研究强调了与肺癌风险相关的特定环境激素毒素.
结论:
- 开发的关联模型为改善肺癌风险护理评估提供了基础.
- 这些发现支持对环境激素毒素进行公共查的必要性.
- 建议使用更大的样本大小进行进一步的外部验证,以确认该模型的有效性.
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