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基于机器学习的预测胃食道结癌使用电子医疗记录

Meng Qian¹, Ying Chen¹, Xiaofen Wu²

¹Department of Gastroenterology, Tongji Institute of Digestive Disease, Tongji Hospital, School of Medicine, Tongji University, 389 Xincun Road, Shanghai, 200065, China.

Clinical and experimental medicine

|August 17, 2025

概括

一个新的工具准确地预测胃食道结癌 (GEJC) 在患有食道症状的患者. 机器学习模型,特别是XGBoost,识别了诸如BMI和食障碍等关键指标,有助于早期诊断.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
胃肠病学胃肠病学

背景情况:

将胃食道结癌 (GEJC) 与其他食道疾病 (如胃食道逆流病 (GERD)) 区分开来,具有临床挑战.
早期和准确的诊断对于有效管理GEJC至关重要.

研究的目的:

开发和验证用于识别患有食道症状的患者的GEJC的预测工具.
利用机器学习和电子医疗记录数据来提高诊断准确度.

主要方法:

对患有GEJC和GERD的患者,分析了2009-2023年的电子病历.
使用倾向得分匹配,然后使用LASSO,Boruta和后勤回归进行特征选择.
四个机器学习模型 (随机森林,GLM,XGBoost,SVM) 经过训练和验证,使用了2:1的训练测试分割.

主要成果:

在测试队列中,XGBoost模型表现出卓越的性能,曲线下的面积 (AUC) 为0.907.
该模型显示出强大的校准和一致性,Brier分数为0.088.8.
重要的预测因素包括体重指数,血红蛋白水平,年龄,反流和消化不良.

结论:

一个强大的机器学习模型被开发用于基于电子医疗记录数据预测GEJC.
这种预测工具有可能指导临床诊断策略,并为GEJC提供及时干预信息.

关键词:

电子医疗记录电子医疗记录胃食道结的癌症胃食道结的癌症胃食道逆流疾病是胃食道逆流疾病.机器学习是机器学习.预测模型的预测模型.

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