基于人工智能的预测性可接受性模型,用于医疗保健系统中有效的疫苗接种
Muhammad Shuaib Qureshi1, Muhammad Bilal Qureshi2, Urooj Iqrar3
1School of Computing Sciences, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Haripur, KPK, Pakistan.
Scientific reports
|November 4, 2024
概括
这项研究引入了一个人工智能模型,以98%的准确度预测疫苗接受率. 影响疫苗接种的关键因素包括社会经济地位,家长教育和地区,指导公共卫生战略.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 接种疫苗的接受对于免疫计划的成功至关重要,但区域差异阻碍了覆盖范围.
- 疫苗接受率低导致免疫率降低,新疫苗的采用速度缓慢.
研究的目的:
- 开发一种可解释的AI驱动的预测模型,用于预测疫苗接受率.
- 将疫苗的可接受性分为不同的类别:低,中等,部分高和高.
- 确定影响疫苗接受的关键人口和社会经济因素.
主要方法:
- 利用了来自PDHS (2017-2018) 的7150个记录和31个属性的数据集.
- 采用LightGBM算法进行预测建模,实现高精度.
- 实现了特征重要性方法,以实现模型可解释性.
主要成果:
- 人工智能模型在预测疫苗可接受性方面取得了98%的准确性.
- 确定了较高的社会经济地位 (SES),地区,父母的职业和母亲的教育作为重要因素.
- 协会规则揭示了这些因素与疫苗接受之间的强烈联系.
结论:
- 人工智能模型提供了准确和可解释的疫苗接受预测.
- 人口和社会经济因素显著影响疫苗接种,为有针对性的干预提供了洞察力.
- 调查结果可以为决策者提供信息,以加强免疫计划策略和改善疫苗覆盖率.
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