Dynamic Hierarchical Aggregation, Selection Bias Tracking, and Detailed Subset Comparison for High-Dimensional Event Sequence Data
Dynamic Hierarchical Aggregation, Selection Bias Tracking, and Detailed Subset Comparison for High-Dimensional Event Sequence Data
复制标题
高维事件序列数据的动态分层聚合、选择偏差跟踪和详细子集比较
DOI：
10.1109/vahc47919.2019.8945029
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
D. Gotz
中科院分区：
文献类型：
--
作者：
Jonathan Zhang;D. Borland;Wenyuan Wang;Joshua Shrestha;D. Gotz
随着时间事件数据，特别是电子健康记录（EHR）数据的收集的增加，已经开发了许多不同的可视化和分析技术来帮助解释这样的数据。随着数据集在事件序列数量和事件类型数量方面都变得越来越大，出现了两个问题：如何对事件类型进行分组，以及如何描述选择队列时可能发生的选择偏差。这张海报总结了两篇被VAST有条件接受的论文，介绍了一种用于分层事件分组的动态交互式算法，一种支持分层探索的有气味的散射加焦点可视化，一种基于树的队列起源可视化，以及一组为队列对提供每维选择偏差信息的可视化[2，4]。这些方法被集成到基于网络的交互式医学分析工具Cadence中
With the increase in collection of temporal event data, especially electronic health record (EHR) data, numerous different visualization and analysis techniques have been developed to assist with the interpretation of such data. As datasets grow increasingly large in both number of event sequences and number of event types, two problems arise: how to group event types, and how to describe selection bias that can occur when selecting cohorts. This poster summarizes two papers, conditionally accepted to VAST, that introduce a dynamic and interactive algorithm for hierarchical event grouping, a scented scatter-plus-focus visualization that supports hierarchical exploration, a tree-based cohort provenance visualization, and a set of visualizations that provide per-dimension selection bias information for pairs of cohorts [2, 4]. These methods are integrated into the web-based interactive medical analysis tool Cadence
影响因子：
1.8
作者：
D. Borland;Wenyuan Wang;D. Gotz
通讯作者：
D. Borland;Wenyuan Wang;D. Gotz
DOI：
10.1109/tvcg.2019.2934661
发表时间：
2019-06
影响因子：
5.2
作者：
D. Gotz;Jonathan Zhang;Wenyuan Wang;Joshua Shrestha;D. Borland
通讯作者：
D. Gotz;Jonathan Zhang;Wenyuan Wang;Joshua Shrestha;D. Borland