FHIR-PYrate:一个数据科学友好的Python包,用于查询FHIR服务器.
René Hosch1,2, Giulia Baldini3,4, Vicky Parmar1,2
1Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany.
BMC health services research
|July 6, 2023
概括
通过自动化队列构建和从电子健康记录中检索数据,FHIR-PYrate简化了用于研究的临床数据提取. 这个Python包简化了这个过程,节省了研究人员宝贵的时间.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 健康 数据科学 数据科学
背景情况:
- 电子病例记录 (EPR) 是现代医疗保健的核心,包含全面的患者病史.
- 临床研究通常涉及重复的,非标准化的过程,用于从EPR中构建研究队列.
- 研究人员目前花费大量时间进行数据提取的编码,从核心研究任务转移注意力.
研究的目的:
- 介绍FHIR-PYrate,一个Python包,旨在简化临床数据收集和提取用于研究.
- 为查询FHIR服务器,下载成像研究和过临床文件提供统一的界面.
- 提高临床研究数据处理流程的效率和性能.
主要方法:
- FHIR-PYrate提供了一个简单的界面,可以与FHIR服务器和REST API进行交互.
- 它可以在所有FHIR资源中实现统一的查询,简化了针对各种用例的定制.
- 集成了并行化和过等功能,以优化数据检索性能.
主要成果:
- 该包方便使用常规CT成像和肺转移乳腺癌患者的临床数据分析预后意义.
- 它使用ICD-10代码,生存信息和额外的临床数据自动收集患者队列.
- 用深度学习模型处理CT扫描和临床数据进行生存分析.
结论:
- FHIR-PYrate能够快速轻松地检索FHIR数据,图像数据,并在医疗文件中进行关键字搜索.
- 该包自动化了研究队伍的组装,大大加快了研究过程.
- 它为研究人员处理复杂的临床数据集提供了一个强大的工具.
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