期刊
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
卷 28, 期 1, 页码 901-911出版社
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2021.3114868
关键词
Aggregates; Visual analytics; Feature extraction; Measurement; Data visualization; Scalability; MIMICs; Temporal event sequence visualization; clustering; hierarchical aggregation; multiple sequence alignment
资金
- CONACYT
- Health Foundation (PathAnalyse project)
- Wellcome Trust [214567/Z/18/Z]
- National Institute for Health Research (NIHR) Applied Research Collaboration Yorkshire and Humber [NIHR200166]
- Wellcome Trust [214567/Z/18/Z] Funding Source: Wellcome Trust
Building a visual overview of temporal event sequences with optimal level-of-detail is an ongoing challenge. This study proposes a technique to build a multilevel overview using hierarchical aggregation and a novel cluster data representation. The technique has been implemented into a visualization system called Sequence Cluster Explorer, allowing users to explore different level-of-detail and inspect data attributes.
Building a visual overview of temporal event sequences with an optimal level-of-detail (i.e. simplified but informative) is an ongoing challenge - expecting the user to zoom into every important aspect of the overview can lead to missing insights. We propose a technique to build a multilevel overview of event sequences, whose granularity can be transformed across sequence clusters (vertical level-of-detail) or longitudinally (horizontal level-of-detail), using hierarchical aggregation and a novel cluster data representation Align-Score-Simplify. By default, the overview shows an optimal number of sequence clusters obtained through the average silhouette width metric - then users are able to explore alternative optimal sequence clusterings. The vertical level-of-detail of the overview changes along with the number of clusters, whilst the horizontal level-of-detail refers to the level of summarization applied to each cluster representation. The proposed technique has been implemented into a visualization system called Sequence Cluster Explorer (Sequen-C) that allows multilevel and detail-on-demand exploration through three coordinated views, and the inspection of data attributes at cluster, unique sequence, and individual sequence level. We present two case studies using real-world datasets in the healthcare domain: CUREd and MIMIC-III; which demonstrate how the technique can aid users to obtain a summary of common and deviating pathways, and explore data attributes for selected patterns.
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