Sex, obesity, diabetes, and exposure to particulate matter among patients with severe asthma: Scientific insights from a comparative analysis of open clinical data sources during a five-day hackathon
Sex, obesity, diabetes, and exposure to particulate matter among patients with severe asthma: Scientific insights from a comparative analysis of open clinical data sources during a five-day hackathon
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DOI：
10.1016/j.jbi.2019.103325
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发表时间：
2019-12-01
影响因子：
4.5
通讯作者：
Peden, David B.
中科院分区：
文献类型：
--
作者：
Fecho, Karamarie;Ahalt, Stanley C.;Peden, David B.
这篇特别的通讯描述了最近由国家促进转化科学中心通过生物医学数据翻译程序（“Translator”）资助的黑客马拉松的活动、产品和经验教训。具体来说，Translator团队成员自我组织并共同努力，在五天的时间里，构思并执行了一项多机构临床研究，旨在利用开放的临床数据来源，检查严重哮喘患者中性别、肥胖、糖尿病和暴露于空气中细颗粒物之间的关系。目标是开发一个概念证明，这种新的协作和数据共享模式可以有效地产生有意义的科学结果并产生新的科学假设。三个Translator临床知识来源，每个都提供开放访问（通过应用程序编程接口），以获取来自主要学术机构电子健康记录系统的数据，作为研究数据的来源。在GitHub存储库中共享的Jupyter Python笔记本用于调用知识来源并分析和集成结果。研究结果重复了性别、肥胖、糖尿病、接触空气中的细颗粒物和严重哮喘之间已建立或怀疑的关系。此外，结果显示了三个翻译临床知识来源之间的具体差异，表明与服务本身或每个服务从中获取患者数据的集水区相关的队列和/或环境特定因素。总的来说，这种特殊的交流展示了激烈的、以团队为导向的黑客马拉松的力量和效用，并提供了学到的一般技术、组织和科学经验。
This special communication describes activities, products, and lessons learned from a recent hackathon that was funded by the National Center for Advancing Translational Sciences via the Biomedical Data Translator program (`Translator'). Specifically, Translator team members self-organized and worked together to conceptualize and execute, over a five-day period, a multi-institutional clinical research study that aimed to examine, using open clinical data sources, relationships between sex, obesity, diabetes, and exposure to airborne fine particulate matter among patients with severe asthma. The goal was to develop a proof of concept that this new model of collaboration and data sharing could effectively produce meaningful scientific results and generate new scientific hypotheses. Three Translator Clinical Knowledge Sources, each of which provides open access (via Application Programming Interfaces) to data derived from the electronic health record systems of major academic institutions, served as the source of study data. Jupyter Python notebooks, shared in GitHub repositories, were used to call the knowledge sources and analyze and integrate the results. The results replicated established or suspected relationships between sex, obesity, diabetes, exposure to airborne fine particulate matter, and severe asthma. In addition, the results demonstrated specific differences across the three Translator Clinical Knowledge Sources, suggesting cohort- and/or environment-specific factors related to the services themselves or the catchment area from which each service derives patient data. Collectively, this special communication demonstrates the power and utility of intense, team-oriented hackathons and offers general technical, organizational, and scientific lessons learned.