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Rare Disease Registries Classification and Characterization: A Data Mining Approach

机译：罕见病登记系统的分类和表征：一种数据挖掘方法

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Background: The European Commission and Patients Organizations identify rare disease registries (RDRs) as strategic instruments to develop research and improve knowledge in the field of rare diseases. Interoperability between RDRs is needed for research activities, validation of therapeutic treatments, and public health actions. Sharing and comparing information requires a uniform and standardized way of data collection, so levels of interconnection between RDRs with similar aims and/or nature of data should be identified. The objective of this study is to define a classification and characterization of RDRs in order to identify different profiles and informative needs. Methods: Exploratory statistical analyses (cluster analysis and random forest) were applied to data derived from the EPIRARE project ('Building Consensus and Synergies for the EU Rare Disease Patient Registration') survey on the activities and needs of RDRs. Results: The cluster analysis identified 3 main typologies of RDRs: public health, clinical and genetic research, and treatment registries. The analysis of the most informative variables, identified by the random forest method, led to the characterization of 3 types of RDRs and the definition of different profiles and informative needs. Conclusions: These results represent a useful source of information to facilitate the harmonization and interconnection of RDRs in accordance with the different profiles identified. It could help sharing the information between RDRs with similar profiles and, whenever possible, interconnections between registries with different profiles. (C) 2015 S. Karger AG, Basel
机译：背景：欧盟委员会和患者组织将稀有疾病登记簿（RDR）确定为发展稀有疾病领域的研究和提高知识水平的战略工具。研究活动，治疗方法验证和公共卫生行动需要RDR之间的互操作性。共享和比较信息需要采用统一和标准化的数据收集方式，因此，应该确定具有相似目标和/或数据性质的RDR之间的互连级别。这项研究的目的是定义RDR的分类和特征，以识别不同的概况和信息需求。方法：将探索性统计分析（聚类分析和随机森林）应用于从EPIRARE项目（“欧盟罕见病患者注册的建筑共识和协同作用”）调查中对RDR的活动和需求进行调查的数据。结果：聚类分析确定了RDR的3种主要类型：公共卫生，临床和基因研究以及治疗登记。通过随机森林方法确定的对信息最丰富的变量的分析，导致了3种RDR的特征以及不同配置文件和信息需求的定义。结论：这些结果代表了有用的信息来源，可根据确定的不同特征促进RDR的协调和相互联系。它可以帮助在具有类似配置文件的RDR之间共享信息，并在可能的情况下，在具有不同配置文件的注册表之间进行互连。 （C）2015 S.Karger AG，巴塞尔

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