一个基于深度学习的创新整体模型,用于预测COVID-19感染
Xiaoying Su1, Yanfeng Sun2, Hongxi Liu1
1School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China.
Scientific reports
|July 29, 2023
概括
这项研究介绍了WOCLSA,一种新的深度学习模型,用于使用患者数据预测COVID-19感染. 与其他整体模型相比,WOCLSA表现出卓越的准确性和效率,有助于公共卫生危机管理.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 流行病学 流行病学
背景情况:
- 全球公共卫生危机需要准确的疾病预测,以有效地分配资源和诊断.
- 目前的COVID-19预测模型,通常是单一或流行病学,显示精度的局限性.
- 现有的整体模型有改善性能的空间,很少有人利用实验室结果进行预测.
研究的目的:
- 开发一种创新的深度学习模型,以提高疾病预测的准确性.
- 解决现有模型在预测COVID-19等传染病方面的局限性.
- 探索患者实验室指标在疾病预测模型中的使用.
主要方法:
- 提出了鱼优化卷积神经网络 (CNN),长短记忆 (LSTM) 和人工神经网络 (ANN) (WOCLSA) 模型.
- 利用鱼优化算法优化参数 (神经元数量,脱落,批量大小) 为集成的ANN,CNN和LSTM模型.
- 采用18个患者指标作为预测指标,并使用训练测试分割验证模型,将WOCLSA与其他三种集体深度学习模型进行比较.
主要成果:
- WOCLSA实现了高预测性能,曲线下的面积 (AUC) 分别达到91%,91%,93%.
- 其他性能指标包括准确性,F1分数,精度和回忆率始终超过91%,超过了可比模型的表现.
- WOCLSA模型在执行时间方面表现出优势,这表明了计算效率.
结论:
- WOCLSA整体模型显示了在帮助验证实验室结果和预测公共卫生事件期间各种疾病的重大潜力.
- 该模型的高精度和效率为加强疾病监测和管理提供了有价值的工具.
- 进一步的研究应该专注于扩大这种先进的医学疾病预测模型的应用.
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