一个深度学习预测模型,用于公众健康问题和对COVID-19疫苗的犹
Heba Mamdouh Farghaly1,2, Mamdouh M Gomaa1,2, Enas Elgeldawi1,2
1Computer Science Department, Faculty of Science, Minia University, Minya, Egypt.
Scientific reports
|June 6, 2023
概括
这项研究开发了一个深度学习模型来预测COVID-19疫苗的副作用和患者的结果,旨在减少疫苗的犹. 这些发现支持基于病史的个性化疫苗选择.
科学领域:
- 计算生物学和生物信息学
- 公共卫生和流行病学.
- 疫苗学 疫苗学 疫苗学
背景情况:
- 由于COVID-19的流行,需要快速开发和部署疫苗.
- 疫苗犹对公共卫生工作构成了重大挑战.
- 了解疫苗副作用对于明智的决策和患者的信任至关重要.
研究的目的:
- 为 COVID-19 疫苗不良事件和患者结果开发一个预测模型.
- 通过考虑个别患者的病史来减少疫苗的犹.
- 为了确定特定的疫苗类型 (Pfizer,Janssen,Moderna) 和不良反应之间的关系.
主要方法:
- 使用了疫苗不良事件报告系统 (VAERS) 数据集.
- 开发了一种深度学习 (DL) 模型,其中包含了Pigeon Swarm优化功能选择.
- 采用循环神经网络 (RNN) 来对患者的结果进行分类 (死亡,住院,康复).
主要成果:
- 在预测患者结果方面取得了高准确性:96.03%的Pfizer-Death,94.7%的Janssen-Hospitalized,和97.79%的Moderna-Recovered.
- 确定了特定的副作用,特别是与中枢神经系统和血液形成系统相关的副作用,这些副作用在某些疫苗类型时增加.
- 该模型在识别疫苗特异性不良事件关系方面显示出有希望的表现.
结论:
- 开发的DL模型有效地预测了疫苗接种后的患者状态,考虑到疫苗类型和不良事件.
- 这些发现支持在基于患者病史的疫苗选择中应用精准医学.
- 该研究为医疗保健提供者提供了宝贵的见解,以解决疫苗犹和个性化疫苗接种策略.
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