一个可重现的基于Python的计算管道,用于实时摄入,高级分析和公共卫生数据的动态报告:一个系统验证研究
Pavan Kumar Krishnapur1, Chaitra Cm1, Arjun B1
1Community and Family Medicine, All India Institute of Medical Sciences, Bhopal, Bhopal, IND.
Cureus
|March 9, 2026
概括
本研究引入了用于公共卫生数据分析的自动化Python管道,从实时数据生成动态报告. 它揭示了对印度的重要见解.
科学领域:
- 公共卫生信息学 公共卫生信息学
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 传统的公共卫生数据分析是低效的,缺乏可重复性.
- 需要自动化工具来弥合实时数据和可操作的见解之间的差距.
- 当前的工作流与手动数据处理和静态报告作斗争.
研究的目的:
- 设计,实施和验证一个完全自动化的Python管道,用于实时数据摄入,分析和动态报告生成.
- 通过基于应用程序编程接口 (API) 的数据集成,为公共卫生信息学创建一个强大的框架.
- 加快公共卫生领域数据驱动的发现和报告.
主要方法:
- 使用Pandas,scikit-learn,statsmodels和python-docx.开发了一个仅用于Python的管道.
- 从公共API获取数据,进行自动重试,进行预处理,并计算复合健康评分.
- 应用K-means集群用于状态分层和相关性分析,生成动态Word报告.
主要成果:
- 管道在约95秒内成功处理了37条记录.
- 确定梅加拉雅州是表现最好的国家,其综合健康评分为100.0.0.
- 揭示了基础设施/服务可用性和人力资源分配之间的显著负相关性.
结论:
- 自动化框架提供了一种高效,可重复的解决方案,用于将公共卫生数据转化为可操作的见解.
- 这种计算框架显著加速了公共卫生和生物信息学中的数据驱动的发现和报告.
- 验证了一种用于自动化公共卫生数据分析和报告的新方法.
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