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集成机器学习模型用于老年人肺癌发病率风险预测:一项追溯纵向研究

Songjing Chen¹, Sizhu Wu²

¹Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

|January 23, 2025

概括

深度Q网络 (DQN) 模型有效预测老年人肺癌风险,识别针对性预防的关键风险因素. 这种方法有助于早期干预,并减少肺癌发病率.

科学领域:

老年学是指老年学的学科.
在瘤学瘤学.
数据科学数据科学数据科学

背景情况:

对老年人的肺癌预防和干预至关重要.
识别高风险个体和预测发病率对于有效的策略至关重要.

研究的目的:

为老年人开发肺癌发病率风险预测模型.
为了促进早期干预和预防肺癌在这个人口.

主要方法:

按年龄和性别分层的人口.
我们比较了六种模型:随机森林,极端梯度增强,深度神经网络,支持矢量机,多重后勤回归和深度Q网络 (DQN).
在2000-2015年数据上训练的模型,内部和外部验证 (2016-2019).

主要成果:

DQN模型显示了各子组的最佳预测性能 (AUROC 0.937-0.953).
使用SHAP值确定高风险因素,包括老年男性的特定遗传突变.
发现65岁以上的男性具有C>A/G>T突变,在戒烟后肺癌发病率显著下降.

结论:

DQN模型适用于高性能肺癌风险预测和老年人高风险因素识别.
提议的早期查和干预途径可以帮助瘤学家制定有针对性的策略.
突出了该方法在预测其他慢性疾病风险方面的潜在应用.

关键词:

年长的老人.发生风险发生风险肺癌是一种肺癌.机器学习机器学习预测预测预测

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