基于贝叶斯网络不确定性推理的肺癌风险预测模型

doi:10.6040/j.issn.1671-7554.0.2022.0927

摘要/Abstract

摘要： 目的将贝叶斯网络与Cox模型相结合,预测包含缺失协变量的个体的肺癌发病风险。方法研究使用的数据来自于英国生物样本库,采用单因素Cox回归分析筛选与肺癌发病相关的预测因素;基于识别出的肺癌潜在预测因素,应用上述联合模型建立个体化肺癌风险预测模型;从鉴别和校准两方面评价模型的预测性能。结果建立的预测模型具有较好的鉴别和校准能力,训练和验证队列的AUC分别为0.854(95%CI:0.836~0.870)和0.885(95%CI:0.871~0.897)。结论本研究构建了基于贝叶斯网络和Cox模型的肺癌风险预测模型;该模型具有良好的鉴别和校准能力,能有效预测肺癌发病高危人群;联合模型在存在缺失预测因子的情况下提供了一种有效的风险预测方法,可为肺癌预防控制提供理论支撑。

关键词: 肺癌, 风险预测模型, 贝叶斯网络, Cox模型, 缺失数据

Abstract: Objective To predict the risk of lung cancer in individuals with missing covariates by combining a Bayesian network with a Cox model. Methods Data were obtained from the UK Biobank. Predictors associated with lung cancer were screened with univariate Cox regression analysis. Based on the predictors identified, the individual risk prediction model of lung cancer was established. The identification and calibration of the model were determined to evaluate its predictive performance. Results The prediction model had good identification and calibration ability, and the AUC of the training and validation cohorts were 0.854(95%CI: 0.836-0.870)and 0.885(95%CI: 0.871-0.897), respectively. Conclusion A lung cancer risk prediction model based on Bayesian network and Cox model was constructed. The model has good identification and calibration ability, and can effectively predict the high-risk population of lung caner. Combined model provides an effective risk prediction method in the presence of missing predictors, which can provide theoretical reference for the prevention and control of lung cancer.

Key words: Lung cancer, Risk prediction model, Bayesian network, Cox model, Missing data

中图分类号:

R730.1

钟璐,薛付忠. 基于贝叶斯网络不确定性推理的肺癌风险预测模型[J]. 山东大学学报 (医学版), 2023, 61(4): 86-94.

ZHONG Lu, XUE Fuzhong. A Lung cancer risk prediction model based on Bayesian network uncertainty inference[J]. Journal of Shandong University (Health Sciences), 2023, 61(4): 86-94.

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