在越南,机器学习的诺米图用于预测登革热冲击综合征在患有登革热的儿科患者中
Rang N Nguyen1, Hue T Lam2, Hung V Phan1
1Pediatrics, Can Tho University of Medicine and Pharmacy, Can Tho, VNM.
Cureus
|May 8, 2025
概括
机器学习模型可以预测儿童登革热休克综合征 (DSS) 风险. 使用白蛋白,APTT,纤维素素,AST和血小板计数的新型名录仪有助于早期识别高风险儿科登革热患者.
科学领域:
- 儿童传染病 儿童传染病
- 临床信息学 临床信息学
- 生物统计学 生物统计学
背景情况:
- 早期识别患有登革热休克综合征 (DSS) 高风险的儿科患者对于及时干预至关重要.
- 机器学习 (ML) 用于预测儿童的DSS风险的应用尚未得到充分探索.
研究的目的:
- 开发和验证一种基于ML的名谱,用于预测儿科登革热患者的DSS风险.
- 支持治疗儿科登革热病例的临床决策.
主要方法:
- 一项对230名越南登革热儿童的前性研究.
- 开发和比较六个ML算法用于DSS风险预测.
- 使用最佳性能模型 (随机森林) 构建名录,该模型基于关键预测因素:白蛋白,激活部分血栓形成时间 (APTT),纤维素原,阿斯巴酸胺转移酶 (AST) 和血小板计数.
主要成果:
- 随机森林模型实现了最高的预测性能,在测试组中,接收器运行特征曲线 (AUROC) 下的面积为0.945.
- 该模型显示了高精度 (0.951),灵敏度 (0.894) 和特异性 (0.976).
- 鉴定出名图的关键预测因素是白蛋白,APTT,纤维素原,AST和血小板计数.
结论:
- 基于ML的模型显示了识别患有发展到DSS的高风险的儿科登革热患者的潜力.
- 开发的名图可以作为一种有价值的临床工具,用于预测儿科实践中的DSS风险.
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