预测COVID-19疫苗接种量使用一个小而可解释的判断和人口学变量集:跨部门认知科学研究
Nicole L Vike1, Sumra Bari1, Leandros Stefanopoulos2,3
1Department of Computer Science, University of Cincinnati, Cincinnati, OH, United States.
了解判断心理学是提高COVID-19疫苗接种率的关键. 机器学习模型显示判断变量显著预测疫苗接种选择,告知公共卫生战略.
科学领域:
- 认知科学 认知科学
- 行为经济学是一种行为经济学.
- 机器学习 机器学习
背景情况:
- 许多人没有接受COVID-19疫苗,尽管有任务.
- 心理因素,特别是奖励和厌恶判断,影响接种疫苗的决定.
- 以前的研究还没有将认知科学判断变量与机器学习相结合,以预测疫苗接种率.
研究的目的:
- 使用机器学习评估判断变量对COVID-19疫苗接种的预测能力.
- 确定影响疫苗接种决策的关键判断概况.
主要方法:
- 对3476名美国成年人进行了人口统计,疫苗接种和预防措施的调查.
- 使用图片评分任务来量化喜欢和不喜欢,模拟判断特征.
- 采用随机森林,平衡随机森林 (BRF) 和后勤回归来预测疫苗吸收.
主要成果:
- 在接种疫苗和未接种疫苗的组之间,人口统计和大多数判断变量存在显著差异.
- 均衡随机森林 (BRF) 显示出高精度 (87.8%) 和AUROC (79%) 的优异性能.
- 判断变量占预测模型中特征重要性的63-75%;年龄,收入和教育调解了这些关系.
结论:
- 判断变量对于理解和预测疫苗选择至关重要.
- 将疫苗教育和信息量身定制为特定的判断配置文件,可以提高接种率.
- 该方法可以通过确定有针对性的干预措施的风险人群来支持公共卫生准备.
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