一个数据驱动的机器学习框架来预测AstraZeneca和sinopharm COVID-19疫苗的副作用
Majid Eterafi1,2, Nasrin Fouladi3, Masoud Amanzadeh4
1Cancer Immunology and Immunotherapy Research Center, Ardabil University of Medical Sciences, Ardabil, Iran.
Scientific reports
|November 19, 2025
概括
机器学习模型可以准确预测COVID-19疫苗的副作用,包括局部和系统反应. 这些工具可以通过分析临床和人口统计数据来个性化疫苗接种策略,减少犹.
科学领域:
- 免疫学 免疫学 免疫学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 广泛的COVID-19疫苗接种需要了解副作用,以解决疫苗犹的问题.
- 机器学习 (ML) 模型提供了强大的工具,可以使用个人数据预测不良事件.
研究的目的:
- 开发和评估用于预测疫苗副作用的ML模型.
- 利用临床和人口统计数据来预测AstraZeneca和Sinopharm COVID-19疫苗后的不良事件.
主要方法:
- 开发和评估各种ML模型,包括支持向量机 (SVM),梯度提升 (GB),XGBoost (XGB),随机森林 (RF),物流回归 (LR) 和人工神经网络 (ANN).
- 分析预测不同剂量疫苗的局部,全身和总副作用.
- 利用SHAP分析确定关键预测因素,如年龄,症状发作和疫苗类型.
主要成果:
- 根据副作用类型和疫苗剂量,ML模型的性能有所不同,局部副作用的AUC值高 (0.77-0.87),全身副作用的AUC值高 (0.75-0.80).
- SVM和RF模型在预测负面影响方面表现出特别有前景,准确度合理.
- 关键预测因素包括年龄,症状发病日,疫苗类型,第一剂效应和症状持续时间.
结论:
- 机器学习模型,特别是SVM和RF,显示了准确预测COVID-19疫苗副作用的巨大潜力.
- 这些预测工具可以帮助制定个性化疫苗接种策略,并改善疫苗接种后监测.
- 来自ML模型的数据驱动洞察力可以通过提供关于副作用的透明信息来帮助缓解公众的疫苗犹.
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