Dynamic Hierarchical Aggregation, Selection Bias Tracking, and Detailed Subset Comparison for High-Dimensional Event Sequence Data

Résumé fourni par la source

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.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.