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相关概念视频

Steps in Outbreak Investigation01:18

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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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.

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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.

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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.