COVID-19再录取的预测建模:来自机器学习和深度学习方法的见解
Wei Kit Loo1, Wingates Voon2, Anwar Suhaimi3
1Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia.
Diagnostics (Basel, Switzerland)
|July 27, 2024
概括
这项研究使用人工智能预测马来西亚的COVID-19再接收风险,确定CatBoost是准确预测患者结果和资源管理的最佳模型.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 公共卫生 公共卫生
背景情况:
- 在全球范围内,COVID-19的再接收对医疗保健系统构成了重大挑战.
- 准确预测再接收风险对于有效的资源配置和患者管理至关重要.
- 马来西亚面临的挑战是管理COVID-19患者护理和防止再入院.
研究的目的:
- 开发和评估人工智能 (AI) 模型,用于预测马来西亚的COVID-19再接收风险.
- 为了比较各种机器学习 (ML) 和深度学习 (DL) 算法在 COVID-19 再入院患者分类中的性能.
- 确定最有效的人工智能技术,以减轻医疗保健资源压力并改善患者的治疗结果.
主要方法:
- 数据集的描述和预处理.
- 数据平衡技术包括随机过量采样 (ROS),边界SMOTE (BSMOTE) 和自适应合成采样 (ADASYN).
- 使用五倍交叉验证的九种ML和十种DL技术的应用和评估,并通过Optuna.com进行超参数优化.
主要成果:
- CatBoost表现出卓越的性能,实现了最高的精度 (0.9882 ± 0.0020) 和AUC (1.0000 ± 0.0000) 与ROS.
- 在BSMOTE和ADASYN数据平衡方法中,CatBoost保持了领先的表现.
- 像SAINT (使用ROS) 和TabNet (使用BSMOTE和ADASYN) 等深度学习模型也表现出强的结果,与决策树组合 (随机森林,XGBoost) 一起.
结论:
- 人工智能,特别是CatBoost模型,在预测马来西亚COVID-19再接收风险方面表现出很高的有效性.
- 开发的方法为医疗保健提供者提供了宝贵的工具,以管理资源和增强患者护理.
- 该研究强调了先进的ML和DL技术在应对传染病带来的公共卫生挑战方面的潜力.
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