[空间流行病学中的病理数据 (REDPath):用于瘤学和服务规划的基于Web的应用程序]
Stephanie Strobl1, Matthias Martin Gaida2,3
1Institut für Pathologie, Universitätsmedizin Mainz, Johannes Gutenberg-Universität Mainz, Langenbeckstr. 1, 55131, Mainz, Deutschland. stephanie.strobl@unimedizin-mainz.de.
Pathologie (Heidelberg, Germany)
|December 10, 2025
概括
REDPath是一个新的基于网络的工具,用于分析空间流行病学的病理数据. 它可视化了癌症负担和医疗保健准入,有助于公共卫生规划和资源分配.
科学领域:
- * 病理学和流行病学
- * 公共卫生和医疗保健规划
- * 空间数据分析
背景情况:
- * 常规病理诊断产生了大量数据集,用于空间流行病学分析尚未开发的潜力.
- *目前的方法无法充分利用病理学数据来研究疾病分布,环境暴露或医疗保健结构.
- *需要易于使用的工具来分析瘤疾病中的空间模式.
研究的目的:
- * 开发REDPath (病理学数据的空间流行病学数据分析),这是一个基于Web的工具,用于病理学数据的空间流行病学分析.
- * 想象不同地理层面的瘤疾病负担和医疗保健供应情况.
- * 支持数据驱动的预防策略,优化医疗保健的资源配置.
主要方法:
- *利用了41707项来自曼茨大学医学中心的瘤诊断 (ICD-10:C00-C97,2019-2025).
- * 综合的人口和环境背景变量.
- *使用C++和Python开发了REDPath,在Leaflet中进行可视化和R (lme4,CARBayes包) 中进行统计分析,通过多层次处理确保数据的保护.
主要成果:
- * REDPath有三个模块:用于交互可视化疾病分布的描述性分析,空间关系和自身相关性的统计模型,以及正在开发的卫生服务模块.
- * 该工具可实现瘤疾病负担的交互映射.
- *空间统计模型可用于识别地理模式和潜在的环境影响.
结论:
- * REDPath 便于对常规病理学数据进行用户友好的空间流行病学分析,不需要专门的统计专业知识.
- * 它的模块化设计允许集成额外的数据,提高其实用性.
- * 该工具作为病理学和流行病学之间的关键接口,直接告知基于证据的医疗保健规划和资源管理.
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