| Literature DB >> 29515937 |
Peter J Polack1, Shang-Tse Chen1, Minsuk Kahng1, Kaya DE Barbaro1, Rahul Basole1, Moushumi Sharmin2, Duen Horng Chau1.
Abstract
The advent of mobile health (mHealth) technologies challenges the capabilities of current visualizations, interactive tools, and algorithms. We present Chronodes, an interactive system that unifies data mining and human-centric visualization techniques to support explorative analysis of longitudinal mHealth data. Chronodes extracts and visualizes frequent event sequences that reveal chronological patterns across multiple participant timelines of mHealth data. It then combines novel interaction and visualization techniques to enable multifocus event sequence analysis, which allows health researchers to interactively define, explore, and compare groups of participant behaviors using event sequence combinations. Through summarizing insights gained from a pilot study with 20 behavioral and biomedical health experts, we discuss Chronodes's efficacy and potential impact in the mHealth domain. Ultimately, we outline important open challenges in mHealth, and offer recommendations and design guidelines for future research.Entities:
Keywords: Applied computing → Health care information systems; Human-centered computing → Visual analytics; Mobile health sensor data; cohort discovery; event alignment; mHealth; sequence mining
Year: 2018 PMID: 29515937 PMCID: PMC5835550 DOI: 10.1145/3152888
Source DB: PubMed Journal: ACM Trans Interact Intell Syst ISSN: 2160-6455