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COVIZ

Authors :
Behrooz Omidvar-Tehrani
Valérie Siroux
Jean-Louis Pépin
Cicero A. L. Pahins
Sihem Amer-Yahia
João Luiz Dihl Comba
Jean-Christian Borel
Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS)
Laboratoire d'Informatique de Grenoble (LIG )
Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])
Institute for Advanced Biosciences / Institut pour l'Avancée des Biosciences (Grenoble) (IAB)
Centre Hospitalier Universitaire [Grenoble] (CHU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Etablissement français du sang - Auvergne-Rhône-Alpes (EFS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])
CHU Grenoble
Agir à dom.
ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)
Centre Hospitalier Universitaire [Grenoble] (CHU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Etablissement français du sang - Auvergne-Rhône-Alpes (EFS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])
Source :
Proceedings of the VLDB Endowment (PVLDB), Proceedings of the VLDB Endowment (PVLDB), VLDB Endowment, 2019, 12 (12), pp.1822-1825. ⟨10.14778/3352063.3352075⟩
Publication Year :
2019
Publisher :
Association for Computing Machinery (ACM), 2019.

Abstract

International audience; We demonstrate COVIZ, an interactive system to visually form and explore patient cohorts. COVIZ seamlessly integrates visual cohort formation and exploration, making it a single destination for hypothesis generation. COVIZ is easy to use by medical experts and offers many features: (1) It provides the ability to isolate patient demographics (e.g., their age group and location), health markers (e.g., their body mass index), and treatments (e.g., Ventilation for respiratory problems), and hence facilitates cohort formation; (2) It summarizes the evolution of treatments of a cohort into health trajectories, and lets medical experts explore those trajectories; (3) It guides them in examining different facets of a cohort and generating hypotheses for future analysis; (4) Finally, it provides the ability to compare the statistics and health trajectories of multiple cohorts at once. COVIZ relies on QDS, a novel data structure that encodes and indexes various data distributions to enable their efficient retrieval. Additionally, COVIZ visualizes air quality data in the regions where patients live to help with data interpretations. We demonstrate two key scenarios. In the ecological scenario, we show how COVIZ can be used to explore patient data to generate hypotheses on the health evolution of cohorts. In the case cross-over scenario, we show how COVIZ can be used to generate hypotheses on cohort health and pollution data. A video demonstration of COVIZ is accessible via http://bit.ly/video-coviz.

Details

ISSN :
21508097
Volume :
12
Database :
OpenAIRE
Journal :
Proceedings of the VLDB Endowment
Accession number :
edsair.doi.dedup.....5bc74de975b3ea161030b1198c1d3a04
Full Text :
https://doi.org/10.14778/3352063.3352075