Iterative cohort analysis and exploration
Iterative cohort analysis and exploration
复制标题
迭代队列分析和探索
DOI：
10.1177/1473871614526077
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发表时间：
2015
影响因子：
2.3
通讯作者：
Adam Perer
中科院分区：
文献类型：
--
作者：
Zhiyuan Zhang;D. Gotz;Adam Perer
队列分析是一种广泛使用的技术，用于调查人群的风险因素。它通常用于在医学，生物信息学和社会科学等领域获得关于人口有趣子集的见解。这些分析的性质随着有关个人的更大数据收集的可用性而不断演变。新兴的大规模数据源的示例包括电子病历系统和社交网络数据集。当领域专家使用如此庞大的数据集执行队列分析时，他们通常依赖技术专家团队来帮助管理和处理数据。这导致缓慢和繁琐的分析过程，其中迭代探索是困难的。为了应对这一挑战，我们正在探索旨在帮助领域专家更独立、更快速地工作的技术。本文介绍了CAVA，一个通过可视化分析进行队列分析的平台。我们介绍了三种主要类型的工件（队列，视图和分析）和一个架构，将这些元素连接在一起，提供一个交互式的探索性分析环境，专为领域专家。除了CAVA设计之外，本文还介绍了来自医疗保健领域的两个用例和一个领域专家评估，以展示我们方法的强大功能。
Cohort analysis is a widely used technique for the investigation of risk factors for groups of people. It is commonly employed to gain insights about interesting subsets of a population in fields such as medicine, bioinformatics, and social science. The nature of these analyses is evolving as larger collections of data about individuals become available. Examples of emerging large-scale data sources include electronic medical record systems and social network datasets. When domain experts perform cohort analyses using such massive datasets, they typically rely on a team of technologists to help manage and process the data. This results in a slow and cumbersome analysis process in which iterative exploration is difficult. To address this challenge, we are exploring technologies designed to help domain experts work more independently and more quickly. This article describes CAVA, a platform for Cohort Analysis via Visual Analytics. We introduce three primary types of artifacts (cohorts, views, and analytics) and an architecture that connects these elements together to provide an interactive exploratory analysis environment designed for domain experts. In addition to the CAVA design, this article presents two use cases from the health-care domain and a domain-expert evaluation to demonstrate the power of our approach.