Demo Data from EDBT 2026 / reviewers in the wild / expert
Valérie Siroux
dblp:247/8063
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0001-7329-7237ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics › visual analytics
visual analytics for healthcare |
0.4 | 1 | 2019 | COVIZ: A System for Visual Formation and Exploration of Patient Cohorts · Proc. VLDB Endow. 2019 |
Information retrieval › retrieval models
query-document similarity |
0.1 | 1 | 2019 | COVIZ: A System for Visual Formation and Exploration of Patient Cohorts · Proc. VLDB Endow. 2019 |
Methods — techniques the papers use, named apart from their topics
interactive visualization · 1.1cohort comparison · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | COVIZ: A System for Visual Formation and Exploration of Patient CohortsabstractWe 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, ecological scenario and case cross-over scenario . A video demonstration of COVIZ is accessible via http://bit.ly/video-coviz. Cícero A. L. Pahins, Behrooz Omidvar-Tehrani, Sihem Amer-Yahia, Valérie Siroux, Jean Louis Pépin, Jean-Christian Borel, João Luiz Dihl Comba |
Proc. VLDB Endow. | 4 |