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使用临床症状和例行检查识别具有慢性阻塞性肺病的非小细胞肺癌:一项回顾性研究

Bing Zhuan^1,2, Hong-Hong Ma^1,2, Bo-Chao Zhang^3,4

¹Department of Respiratory Medicine, Ningxia Hui Autonomous Region People's Hospital, Yinchuan, Ningxia, China.

Frontiers in oncology

|August 14, 2023

概括

机器学习模型可以使用临床数据识别患有非小细胞肺癌 (NSCLC) 和慢性阻塞性肺病 (COPD) 的患者. 支持矢量机器模型在预测并发诊断方面表现出最佳性能.

科学领域:

医疗信息学医疗信息学
在瘤学瘤学.
肺部病理学肺部病理学

背景情况:

非小细胞肺癌 (NSCLC) 和慢性阻塞性肺病 (COPD) 在早期阶段具有生理上的相似性.
同时患有NSCLC和COPD的患者的预后较差,包括生存率降低和死亡率增加.

研究的目的:

开发和比较机器学习模型,用于预测未来的COPD结合NSCLC的诊断.
为了利用患者疾病和常规临床数据进行预测建模.

主要方法:

采用了六种机器学习算法:K-最近邻居,逻辑回归,极端梯度增强,支持矢量机,天真贝叶斯和人工神经网络.
模型被训练并使用237名患者的数据进行评估 (135名NSCLC和COPD患者,仅有102名NSCLC患者).
性能指标包括灵敏度,特异性,准确性,F1分数,MCC,卡帕,AUROC和AUPRC.

主要成果:

肺功能和肺气被确定为同时发生NSCLC和COPD的显著风险因素.
支持矢量机 (SVM) 模型实现了最高的性能,精度为0.946,AUROC为0.975,AUPRC为0.987.
该SVM模型展示了优秀的预测能力,用于识别结合NSCLC和COPD的患者.

结论:

整合临床症状和例行检查数据的机器学习模型对于确定COPD患者的NSCLC风险是有效的.
开发的模型为早期风险评估和诊断COPD患者同时发生的NSCLC提供了有价值的工具.

关键词:

慢性慢性肺炎是一种慢性慢性肺炎,COPD是一种慢性肺炎.在NSCLCLC中,我们可以看到NSCLCLC.检测检测检测检测检测突发性质炎症是什么意思标识标识标识标识标识机器学习是机器学习.肺功能肺功能肺功能

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