人工智能应用于大流行期间的分析:巴西里约格兰德多诺尔特州的COVID-19病床占用率
Tiago de Oliveira Barreto1, Nícolas Vinícius Rodrigues Veras1,2, Pablo Holanda Cardoso1,2
1Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil.
Frontiers in artificial intelligence
|December 25, 2023
概括
评估了机器和深度学习模型,以使用RegulaRN平台数据预测COVID-19患者的结果. 多层感知器和根平均平方传播模型显示出强大的预测性能,有助于临床决策.
科学领域:
- * 计算健康信息学
- * 人工智能在公共卫生中的应用
背景情况:
- *COVID-19大流行给全球健康带来了重大挑战.
- *巴西里约格兰德多诺尔特州的RegulaRN平台管理了COVID-19患者的床位分配.
- *在健康危机期间,有效预测患者的结果至关重要.
研究的目的:
- * 确定最佳的机器学习和深度学习模型来预测COVID-19患者的结果.
- * 分析RegulaRN平台数据以检测患者床位调节模式.
- * 增强卫生专业人员和机构的决策能力.
主要方法:
- *从RegulaRN平台 (2020年4月至2022年8月) 分析了25366个床位的规定.
- *从20个可用的功能中选择了9个关键功能,不包括缺失的数据.
- *应用数据预处理,平衡,培训和测试协议.
- *各种机器学习和深度学习算法的比较.
主要成果:
- * 具有随机梯度下降的多层感知器实现了84.01%的精度,79.57%的精度和81.00%的F1分数.
- *根平均平方传播产生了最好的回忆 (84.67%),特异性 (84.67%) 和ROC-AUC (91.6%).
- *确定了用于分类COVID-19患者病床调节数据的优质模型.
结论:
- *机器和深度学习模型可以有效地使用现实世界的健康数据预测COVID-19患者的结果.
- * 该研究确定了提供高性能指标的特定模型 (MLP,RMSProp).
- *研究结果支持整合人工智能驱动的数字健康解决方案,用于危机管理和临床决策支持.
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