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血小板代谢物作为毒症诊断和管理中的候选生物标志物,使用拟议的可解释的人工智能方法

Fatma Hilal Yagin¹, Umran Aygun², Abdulmohsen Algarni³

¹Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya 44280, Türkiye.

Journal of clinical medicine

|September 14, 2024

概括

使用机器学习和可解释的AI识别的新型血小板代谢物显示出早期败血症诊断的希望. 这种方法有助于监测败血症并通过精确定位关键生物标志物,如卡尼丁和谷氨酸酸,改善患者的治疗结果.

科学领域:

生物化学生化学
计算生物学计算生物学
免疫学免疫学免疫学

背景情况:

败血症是一个重大的全球健康挑战,死亡率高.
目前的败血症诊断方法缺乏足够的敏感性和特异性.
血小板具有超出血液静止的重要免疫功能,使其成为潜在的诊断目标.

研究的目的:

开发一个整合机器学习 (ML) 和可解释的人工智能 (XAI) 的预测模型.
为了确定早期败血症诊断的新型血小板代谢标志物.
为了利用XAI对ML模型预测的直观理解.

主要方法:

定量1H核磁共振 (NMR) 用于分析39名参与者 (25名败血症,14名对照) 的血小板代谢物概况.
合成少数人过量抽样方法 (SMOTE) - Tomek和随机森林归算解决了数据不平衡和缺失的值.
极端梯度提升 (XGBoost),光梯度提升机 (LightGBM) 和内核树提升 (KTBoost) 模型被用于败血症预测,并通过交叉验证进行验证.

主要成果:

该KTBoost模型表现出卓越的性能,在败血症诊断中准确度为0.900和AUC为0.943.
莎普利添加剂测试 (SHAP) 确定了卡尼丁,谷氨酸和肌氨酸醇作为关键的预测生物标志物.
XAI为模型的诊断决策提供了直观的解释.

结论:

通过KTBoost和XAI分析的血小板代谢物,为早期败血症检测和监测提供了显著的潜力.
鉴定的生物标志物可以通过及时干预,帮助改善患者的治疗结果.
这种综合方法增强了代谢学在败血症管理中的临床实用性.

关键词:

生物标志物生物标志物可解释的人工智能机器学习是机器学习.血小板代谢组的代谢组是什么已经有了血症.

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