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基于机器学习的预测模型用于DKACOVID-19患者的预后
Zhongyuan Xiang1, Jingyi Hu2, Shengfang Bu3
1Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China. xiangzhongyuan@csu.edu.cn.
患有COVID-19的糖尿病酸症 (DKA) 患者面临更高的严重疾病和死亡风险. 机器学习模型,特别是逻辑回归,可以准确地预测这些患者的严重疾病进展和死亡.
科学领域:
- * 医学信息学 医学信息学
- * 计算生物学 * 计算生物学
- * 传染病 传染病
背景情况:
- * 糖尿病酸性脂肪酸症 (DKA) 与2019年新冠肺炎 (COVID-19) 患者的严重疾病和死亡率的增加有关.
- *有效预测DKA的COVID-19患者的严重结果对于及时的临床干预至关重要.
研究的目的:
- * 开发和评估机器学习模型,用于预测COVID-19患有DKA的患者的严重疾病和死亡率.
- * 确定该患者队列中严重结果的关键预测因子.
主要方法:
- *利用了来自242名患有DKA的COVID-19患者的血液样本和临床数据.
- * 开发并比较了五种机器学习模型:极端梯度提升 (XGB),物流回归 (LR),随机森林 (RF),支持矢量机 (SVM) 和多层感知器 (MLP).
- * 采用5倍交叉验证和沙普利增量解释 (SHAP) 来进行模型评估和解释.
主要成果:
- * 后勤回归 (LR) 模型在预测死亡率方面取得了最高的准确性 (AUC = 0.933).
- * LR模型还在预测疾病进展到严重疾病方面表现出很高的性能 (AUC = 0.898).
- * SHAP分析为模型的预测提供了可解释性.
结论:
- *机器学习模型,特别是LR,可以有效地预测COVID-19患有DKA的患者的严重结果.
- *开发的预测模型可以帮助临床医生为这种高风险人群做出明智的治疗决策.
- *需要进一步验证和实施这些模型.
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