使用机器学习算法预测结核性多叶流液的诊断和比较性能
Yanqing Liu1, Zhigang Liang2, Jing Yang3
1Department of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
International journal of medical informatics
|December 20, 2023
概括
机器学习有助于早期诊断结核性肺溢液 (TPE). 一种支持向量机 (SVM) 模型显示出高精度,改善了患者的预后和治疗策略.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 机器学习在医疗保健中的应用
- 肺部病理学 肺部病理学
背景情况:
- 结核性流 (TPE) 的早期诊断至关重要,但具有挑战性.
- 准确的差异诊断影响患者的预后和治疗疗效.
研究的目的:
- 开发和比较9个机器学习 (ML) 算法用于早期TPE诊断.
- 为了确定TPE歧视的最佳ML模型.
主要方法:
- 追溯分析1435名患有多发性溢液 (PE) 的患者.
- 使用九个ML算法进行特征选择和模型开发.
- 使用外部数据和SHAP分析验证最佳模型.
主要成果:
- 支持矢量机 (SVM) 实现了最高的性能 (AUC 0.914).
- 外部验证证实了SVM的诊断准确性 (AUC为0.898).
- 关键的诊断特征包括PE腺脱氨酶 (ADA),PE癌胚抗原 (CEA) 和血清CYFRA21-1.
结论:
- 一个经过验证的SVM模型显示了早期TPE诊断的前景.
- 这种ML方法可以支持TPE管理的临床决策.
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