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早期检测非小细胞肺癌:一个电子健康记录数据驱动的方法

Xiudi Li¹, Erin Y Yuan², Stephen J Kuperberg³

¹Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.

|October 11, 2025

概括

这项研究使用电子健康记录 (EHR) 开发了非小细胞肺癌 (NSCLC) 的早期检测模型. 该模型识别高风险个体进行个性化查,改善癌症预防和诊断.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
机器学习机器学习

背景情况:

个性化医疗保健对于管理癌症风险至关重要.
电子健康记录 (EHR) 数据可以增强癌症预防和早期诊断.
识别高风险个体可以实现量身定制的临床管理和及时干预.

研究的目的:

开发一种用于早期检测非小细胞肺癌 (NSCLC) 的预测模型.
利用电子健康记录 (EHR) 数据来识别预测特征.
改善个性化癌症查和早期诊断.

主要方法:

使用了来自马萨诸塞州布里格姆总医院的EHR数据.
实施了三阶段合体学习方法.
使用后勤回归和前性考克斯模型生成风险评分.

主要成果:

确定了127个来自EHR的特征预测NSCLC,包括吸烟史,实验室结果和慢性肺部疾病.
实现了0.801的ROC曲线 (AUC) 下的面积,用于预测1年的NSCLC风险 (18岁以上的人口).
在40岁以上的人群中,AUC达到0.757.

结论:

来自EHR的特征可以预测早期NSCLC的诊断.
开发的风险预测模型的表现优于仅依赖人口统计和吸烟数据的基线模型.
纳入EHR数据显示了个性化癌症查建议和早期检测的潜力.

关键词:

早期癌症检测早期癌症检测电子健康记录是电子健康记录.非小细胞肺癌是非小细胞肺癌.风险预测模型的风险预测模型.

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