一个基于风险因素的高精度XGBoost多民族模型用于识别患有皮肤癌的患者
Matteo D'Antonio1, Wilfredo G Gonzalez Rivera2,3, Robert A Greenes2,4
1Department of Medicine, Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA. mdanto@uw.edu.
Nature communications
|October 30, 2025
概括
这项研究确定了遗传血统,生活方式,健康的社会决定因素和PDE5a抑制剂的使用作为关键的皮肤癌风险因素. 一个XGBoost模型改善了不同人群的早期检测,旨在减少健康结果差异.
科学领域:
- 在瘤学瘤学.
- 遗传学 是一个遗传学.
- 公共卫生 公共卫生
背景情况:
- 欧洲血统的个人患皮肤癌的发病率更高.
- 非欧洲祖先在诊断时经常出现更先进的皮肤癌,导致结果的差异.
研究的目的:
- 在不同的人群中识别皮肤癌的独立风险因素.
- 开发一种多民族预测模型,用于早期发现皮肤癌.
- 为了减少非欧洲祖先的结果差异.
主要方法:
- 利用多样化的"我们所有人"数据集.
- 采用XGBoost多民族模型,整合遗传祖先,生活方式,健康的社会决定因素和PDE5a抑制剂的使用.
- 进行沙普利评分分析以了解风险因素相互作用.
主要成果:
- 鉴定了遗传祖先,生活方式,健康的社会决定因素和PDE5a抑制剂的使用作为独立的皮肤癌风险因素.
- XGBoost模型在识别皮肤癌患者方面表现出高准确度.
- 在年龄和癌症史,遗传血统和收入等风险因素之间发现了强烈的非线性关联,特别是在年轻人中.
结论:
- 开发的XGBoost多民族模型为早期皮肤癌检测提供了精准医学方法.
- 这种方法有可能显著减少种族多样性患者的结果差异.
- 了解遗传和社会决定因素的相互作用对于年轻人群的早期检测至关重要.
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