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机器学习用于预测COVID-19急性损伤患者慢性病进展:可行性研究

Carlos Gracida-Osorno¹, Gloria María Molina-Salinas², Roxana Góngora-Hernández³

¹Servicio de Medicina Interna, Hospital General Regional No. 1, CMN Ignacio García Téllez, Instituto Mexicano del Seguro Social, Mérida 97150, Mexico.

|July 27, 2024

概括

机器学习模型可以预测COVID-19急性损伤 (AKI) 患者的慢性病 (CKD) 进展情况. 提升和后勤回归模型显示出高准确度,有助于早期检测和管理.

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
人工智能的人工智能

背景情况:

冠状病毒疾病 (COVID-19) 可能导致急性损伤 (AKI),增加慢性病 (CKD) 的风险.
在AKI的COVID-19患者中评估CKD进展对于及时干预至关重要.

研究的目的:

评估机器学习 (ML) 模型的可行性,以预测AKI的COVID-19患者的CKD进展.
在这个预测任务中比较不同ML算法的性能.

主要方法:

在墨西哥尤卡坦州梅里达的一家医院住院接受AKI的成年COVID-19患者的回顾性研究.
开发和验证四个ML模型:支持矢量机 (SVM),随机森林,后勤回归和提升.
利用变量选择方法来优化模型性能.

主要成果:

提升模型实现了0.841的接收器运行特征曲线 (AUC-ROC) 下的最高面积,紧随其后的是物流回归 (0.840).
支持矢量机 (SVM) 模型显示出优异的分类率: 99.8% ± 0.1 (培训) 和 98.43% ± 1.79 (验证).
所有模型在预测CKD进展方面都表现出有希望的表现.

结论:

机器学习模型可用于评估AKI的COVID-19患者的CKD进展.
提升和后勤回归模型对在这个人群中早期发现和管理CKD特别有希望.
需要进一步的研究来验证这些发现,并完善预测能力.

关键词:

在 COVID-19 疫情中,急性脏损伤急性脏损伤是什么提升刺激的提升.慢性病的进展和进展逻辑回归的逻辑回归方法机器学习是机器学习.预测模型预测模型随机的森林随机的森林支持矢量机器的支持矢量机器.

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