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一个机器学习框架,以有效地对长期COVID患者进行选,并将其提供给专家

Argyrios Kerezis, Archontoula Antonoglou, Andreas Asimakos

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

机器学习有效地优先考虑长期COVID患者的专业护理. 支持矢量机模型将一般健康,疼痛和焦虑确定为关键因素,改善了医疗保健资源配置.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
临床决策支持系统临床决策支持系统

背景情况:

长期COVID对医疗保健系统构成重大挑战,需要优化患者管理.
有效的分组对于有效地分配专业资源 (心脏病学,肺病学,精神病学) 是至关重要的.

研究的目的:

开发和评估一个机器学习 (ML) 框架,以优先考虑长期COVID患者的专家咨询.
确定影响患者针对性护理分类的关键临床因素.

主要方法:

使用175名长期COVID患者的数据集开发了一个支持矢量机器 (SVM) 模型.
为了解决阶级不平衡,采用了合成少数群体过量采样技术 (SMOTE).
模型的性能与随机森林,k-最近邻居,XGBoost和决策树进行了比较.

主要成果:

SVM模型的精度为0.67,曲线下的面积 (AUC) 为0.84.
该模型的性能优于其他评估的ML算法.
SF-36一般健康评分,身体疼痛和焦虑水平被确定为最重要的预测因素.

结论:

基于ML的分类可以提高长期COVID患者专家咨询的效率.
这种方法有可能优化医疗保健资源配置并改善患者的治疗结果.
需要进一步的前性研究来评估这一ML框架的临床影响.

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