Sequen-C: A Multilevel Overview of Temporal Event Sequences

TL;DR

Sequen-C introduces a multilevel visualization technique for temporal event sequences, enabling users to explore sequence clusters at various levels of detail through hierarchical aggregation and novel data representation, aiding insight discovery.

Contribution

The paper presents Sequen-C, a visualization system with a new hierarchical aggregation method and cluster representation for multilevel exploration of temporal sequences.

Findings

Effective multilevel overview of healthcare event sequences

Supports exploration of common and deviating pathways

Facilitates attribute analysis at multiple levels

Abstract

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.