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在慢性病患者中,使用多种机器学习算法对COVID-19死亡风险的预测建模

Lin Luo¹, Peng Gao¹, Chunhui Yang²

¹Department of Clinical Laboratory, Second Affiliated Hospital of Dalian Medical University, No.467, Zhongshan Road, Shahekou District, Dalian, 116027, Liaoning, China.

Scientific reports

|November 6, 2024

概括

这项研究创建了一个机器学习模型来预测慢性病 (CKD) 的COVID-19患者的死亡风险. 该模型准确地识别高风险个体,以改善临床管理.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
传染性疾病传染性疾病
医疗保健中的机器学习

背景情况:

冠状病毒病2019 (COVID-19) 对患有慢性病 (CKD) 的患者构成重大死亡风险.
在CKD患者中,现有的COVID-19风险分层需要优化个性化临床管理.

研究的目的:

开发和验证一种机器学习预测模型,用于评估患有CKD的COVID-19患者的死亡风险.
为了确定临床实验室的关键变量预测死亡率在这个弱势群体.

主要方法:

对219名患有CKD的COVID-19患者的临床实验室数据的回顾性分析.
开发和评估机器学习算法,包括LightGBM.
使用校准曲线,决策曲线分析和ROC曲线进行模型性能评估.

主要成果:

光GBM模型显示出强大的预测性能 (AUC=0.833,灵敏度=0.952,特异性=0.714).
关键预测因素包括前白蛋白,中性粒细胞百分比,呼吸指数,氧和度,血清二氧化碳,葡萄糖,中性粒细胞数量和尿酸.
在46名患者的外部验证证实了该模型的可接受准确性.

结论:

开发的机器学习模型是查高风险COVID-19CKD患者的强大工具.
这个模型可以支持临床决策,优化资源配置,并促进及时干预,以改善患者的治疗结果.

关键词:

在 COVID-19 疫情中,慢性脏疾病慢性脏疾病轻GBM (光梯度增强机器) 的使用.机器学习算法机器学习算法预测模型的预测模型.

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