健康分析数据到证据套件 (HADES):用于观测研究的开源软件
Martijn Schuemie1,2,3, Jenna Reps1,2,4, Adam Black1,5
1Observational Health Data Science and Informatics, New York, NY, USA.
Studies in health technology and informatics
|January 25, 2024
概括
健康分析数据到证据套件 (HADES) 是一个开源工具,用于分析现实世界的健康数据. 它可以在联邦网络中进行强有力的研究,支持监管决策.
科学领域:
- 医疗信息学 医疗信息学
- 观察性健康数据科学数据科学
- 现实世界的证据生成
背景情况:
- 观察健康数据科学和信息学 (OHDSI) 社区开发了健康分析数据到证据套件 (HADES).
- 哈德斯 (HADES) 是一个开源软件集合,旨在分析观察性健康数据.
- 它运行数据转化为观察医学结果伙伴关系 (OMOP) 共同数据模型.
研究的目的:
- 介绍和描述HADES软件套件的功能.
- 突出HADES在实现对现实世界健康数据的先进分析方面的作用.
- 展示其在联合数据网络和监管决策中的实用性.
主要方法:
- 哈德斯直接对转换到OMOP CDM的医疗保健数据 (例如,EHR,索赔) 执行高级分析.
- 它支持表征,人口级因果效应估计和患者级预测.
- 该软件旨在实现广泛的技术兼容性,并利用连续集成与广泛的单元可靠性测试.
主要成果:
- 哈德斯 (HADES) 便于在联合数据网络中进行分析,通过仅共享聚合统计数据来保护数据隐私.
- 它在各种技术环境中实施,确保广泛适用.
- 该套件遵循OHDSI的最佳实践,并且是大多数发表的OHDSI研究的组成部分.
结论:
- 哈德斯 (HADES) 是一套可靠,多功能,开源的软件套件,对于健康信息学中的现实世界证据生成至关重要.
- 它在联合网络中的应用增强了数据隐私和分析能力.
- 哈德斯在推进观察性健康数据科学方面发挥着重要作用,并为监管决策提供了信息.
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