提高免疫的先天错误的预测:使用机器学习将杰弗里模型基础标准与临床变量集成到机器学习中
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Predictive Immune Modeling of Solid Tumors
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Alaaddin Yorulmaz1, Ali Şahin2, Gamze Sonmez3
1Department of Pediatrics, Selçuk University Medical School, Konya 42250, Turkey.
Children (Basel, Switzerland)
|September 27, 2025
概括
机器学习模型通过将临床数据与杰弗里模型基金会 (JMF) 标准相结合,显著改善了先天免疫错误 (IEI) 的预测. 这种方法提高了IEI的诊断准确性,有助于早期检测和干预.
科学领域:
- 免疫学 免疫学 免疫学
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 免疫的先天性错误 (IEI) 是一种影响免疫系统的罕见遗传疾病.
- 杰弗里模型基金会 (JMF) 的十个警告标志有助于早期检测IEI,但敏感性有限.
- 机器学习 (ML) 通过结合各种临床变量来提高IEI预测的潜力.
研究的目的:
- 评估ML算法在预测IEI方面的有效性.
- 为了比较单独使用JMF标准的诊断性能与JMF标准与其他临床变量相结合,使用ML.
- 通过使用ML解释性技术,识别IEI的关键临床预测因素.
主要方法:
- 在临床免疫学诊所评估的298名参与者 (98名IEI,200名非IEI) 的回顾性分析.
- 使用嵌套的5倍交叉验证,训练和优化四个ML算法 (随机森林,k-NN,SVM,天真贝叶斯).
- 使用精度,灵敏度,特异性,F1得分,Youden指数和AUROC进行性能评估;使用SHAP评估的特征重要性.
主要成果:
- 支持矢量机 (SVM) 模型在将JMF标准与额外的临床变量集成时获得了卓越的性能 (精度:0.94,灵敏度:0.97,AUROC:0.99).
- 这种组合的ML模型显著超过了经典的JMF标准 (精度:0.91,灵敏度:0.87,AUROC:0.90) 和一个仅使用JMF的ML模型.
- SHAP分析强调了早期死亡的家族史,肺炎史和ICU入院作为IEI的关键预测因素.
结论:
- 机器学习模型,特别是将JMF标准与临床变量集成的SVM,大大提高了IEI预测的准确性.
- 在临床实践中实施这些ML模型可以促进早期IEI诊断和及时治疗.
- 改进的诊断能力可能会减少患者的发病率和与IEI相关的整体医疗负担.
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