Demo Data from EDBT 2026 / reviewers in the wild / expert
Zuoqin Yan
dblp:383/6769
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
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.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics › interactive visualization
interactive visualization system |
0.8 | 1 | 2024 | A Two-Phase Visualization System for Continuous Human-AI Collaboration in Sequelae Analysis and Modeling · IEEE VIS 2024 |
Medical and health informatics
clinical data analysis |
0.2 | 1 | 2024 | A Two-Phase Visualization System for Continuous Human-AI Collaboration in Sequelae Analysis and Modeling · IEEE VIS 2024 |
Methods — techniques the papers use, named apart from their topics
risk assessment · 1.5formative study · 1.5anomaly detection · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | A Two-Phase Visualization System for Continuous Human-AI Collaboration in Sequelae Analysis and ModelingabstractIn healthcare, AI techniques are widely used for tasks like risk assessment and anomaly detection. Despite AI’s potential as a valuable assistant, its role in complex medical data analysis often over-simplifies human-AI collaboration dynamics. To address this, we collaborated with a local hospital, engaging six physicians and one data scientist in a formative study. From this collaboration, we propose a framework integrating two-phase interactive visualization systems: one for Human-Led, AI-Assisted Retrospective Analysis and another for AI-Mediated, Human-Reviewed Iterative Modeling. This framework aims to enhance understanding and discussion around effective human-AI collaboration in healthcare. Yang Ouyang, Chenyang Zhang 0002, He Wang 0053, Tianle Ma, Chang Jiang 0001, Yuheng Yan, Zuoqin Yan, Xiaojuan Ma, Chuhan Shi, Quan Li 0002 |
IEEE VIS | 7 |