将原始数据转换为有意义的信息以增强数据重用的特征提取过程的标准化描述:卡塔尔世界杯8强波胆分析共识研究%A Lamer,Antoine %A Fruchart,Mathilde %A Paris,Nicolas %A Popoff,Benjamin %A Payen,Anaïs %A Balcaen,Thibaut %A Gacquer,William %A Bouzillé，Guillaume %A Cuggia,Marc %A Doutreligne,Matthieu %A Chazard,Emmanuel %+里尔大学，CHU里尔，ULR 2694 - METRICS:Évaluation des Technologies de santé et des Pratiques médicales, 1 place de Verdun，里尔，59000，法国，33 320626969,antoine.lamer@univ-lille.fr %K特征提取%K数据重用%K数据仓库%K数据库%K算法%K观察医疗结果伙伴关系%D 2022 %7 17.10.2022 %9原创论文%J JMIR Med Inform %G英文%X背景:尽管数据重用提供了许多机会，但其实现存在许多困难，原始数据不能直接重用。信息并不总是在源数据库中直接可用，需要在定义算法时使用原始数据进行计算。目的:本文的主要目的是在进行回顾性观察研究时，对特征提取过程中所需的步骤和转换进行标准化描述。第二个目标是确定如何在数据仓库的模式中存储特性。方法:本研究主要包括以下3个步骤:(1)收集与特征提取相关的相关研究案例，并基于数据的自动二次利用;(2)研究案例中常见的原始数据、步骤和转换的标准化描述;(3)确定一个合适的表来存储观察医疗结果伙伴关系(OMOP)公共数据模型(CDM)中的特征。结果:我们采访了来自3所法国大学医院和一个国家机构的10名研究人员，他们参与了8项回顾性和观察性研究。基于这些研究，出现了2种状态(航迹和特征)和2种转换(航迹定义和航迹聚合)。 “Track” is a time-dependent signal or period of interest, defined by a statistical unit, a value, and 2 milestones (a start event and an end event). “Feature” is time-independent high-level information with dimensionality identical to the statistical unit of the study, defined by a label and a value. The time dimension has become implicit in the value or name of the variable. We propose the 2 tables “TRACK” and “FEATURE” to store variables obtained in feature extraction and extend the OMOP CDM. Conclusions: We propose a standardized description of the feature extraction process. The process combined the 2 steps of track definition and track aggregation. By dividing the feature extraction into these 2 steps, difficulty was managed during track definition. The standardization of tracks requires great expertise with regard to the data, but allows the application of an infinite number of complex transformations. On the contrary, track aggregation is a very simple operation with a finite number of possibilities. A complete description of these steps could enhance the reproducibility of retrospective studies. %M 36251369 %R 10.2196/38936 %U https://medinform.www.mybigtv.com/2022/10/e38936 %U https://doi.org/10.2196/38936 %U http://www.ncbi.nlm.nih.gov/pubmed/36251369