邻里问题:通过可解释的AI预测肺癌查遵守情况
Kun-Han Lu1, Yi Xiao2, Aamna Akhtar3
1Department of Applied AI and Data Science, City of Hope, Duarte, CA, USA; Division of Mathematics for Cancer Evolution and Early Detection, Beckman Research Institute, City of Hope, Duarte, CA, USA.
Lung cancer (Amsterdam, Netherlands)
|December 22, 2025
概括
肺癌查 (LCS) 的坚持是由健康的社会决定因素 (SDOH) 预测的. 贫困等邻里因素是不遵守的关键预测因素,为高风险个人提供有针对性的干预信息.
科学领域:
- 公共卫生 公共卫生
- 机器学习 机器学习
- 健康差异 在健康上的差异
背景情况:
- 肺癌查 (LCS) 的遵守对于高风险人群的早期检测至关重要.
- 健康的社会决定因素 (SDOH) 显著影响医疗保健的获取和坚持.
- 预测建模可以识别患有LCS不遵守风险的个体.
研究的目的:
- 使用SDOH数据开发肺癌查遵守率的预测模型.
- 确定与LCS不遵守相关的关键个人和社区层面的因素.
- 为了改善那些不太可能完成年度LCS后续扫描的人的风险分层.
主要方法:
- 招募188名高风险,少数群体的个人进行低剂量计算机断层扫描 (LDCT) 查.
- 通过调查收集人口统计,烟草使用,社会需求和风险感知数据.
- 利用XGBoost分类器与SHAP分析来预测基于个人和地理编码的社区SDOH指标的LCS遵守.
主要成果:
- 研究队列包括各种少数群体,LCS不遵守率为66%.
- 预测模型取得了强的表现 (AUROC 0.81,AUPRC 0.90).
- 社区SDOH因素 (例如,学业能力,贫困) 比个人因素更能预测不遵守.
结论:
- 机器学习使用SDOH准确地预测LCS非坚持.
- 社区层面的特征对于为LCS遵守干预提供信息至关重要.
- 针对区域的策略可以改善高风险人群的LCS坚持.
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