Next month I’ll be attending the 2016 AMIA Annual Symposium in Chicago. AMIA will be host this year to the 2016 Visual Analytics in Healthcare Workshop. This year’s workshop will be the 7th annual workshop, and will mark a return to AMIA after IEEE VIS hosted last year’s event. I am a co-author for two papers at this year’s workshop.
First, I am lead author on a progress report from the AMIA VIS Working Group’s Task Force on Evaluation.
My paper titled “Soft Patterns: Moving Beyond Explicit Sequential Patterns During Visual Analysis of Longitudinal Event Datasets” has been accepted as part of the IEEE VIS 2016 Workshop on Temporal & Sequential Event Analysis.
The workshop is being held in conjunction with the main IEEE VIS conference in Baltimore, Maryland. VIS runs from October 23-26, and the workshop takes place on Monday October 24.
All papers accepted to the workshop should appear on the event’s website in advance of the actual workshop.
While there are a variety of Javascript libraries available for creating visualizations, the most widely used is D3.js.
D3 stands for Data-Driven Documents, and at the core of D3 is a programming model in which users join data elements to document elements. These are often SVG elements, but D3 can really be used for any kind of data-driven DOM manipulation. I instruct my students on the basics of D3.js as part of the Visual Analytics course which I teach every fall.
I’m co-chairing a workshop at IEEE VIS this year which focuses on the visual analysis of temporal or sequential events. This has been a topic that I’ve focused on for a while now, and the organizing includes others from both industry and academia who are interested in the topic. I’ve copied the introduction to the CFP below, and I encourage you to visit the workshop’s website to learn more .
The latest issue of IEEE Computer Graphics and Applications (CG&A) contains an article I co-wrote with David Borland at RENCI about the data-driven future of healthcare and the role that data visualization and visual analytics can play in this ongoing transformation. The article can be read online via this link, or downloaded via the IEEE Xplore Digital Library.
Risk is a difficult concept, in part because of the many ways it is measured. Interpreting specific numbers can be unintuitive even for people who do it every day, leading to critical decisions being made based on faulty interpretations of evidence.
This is a place where data visualization can play a crucial role. A recent article in Science makes a similar point, with a graphic example of how to help people understand that even “accurate” medical tests can be misleading.
I just returned from the 2016 ACM Intelligent User Interfaces conference which was held in Sonoma, California. As I’ve previously written, we at the VACLab had a paper published as part of the proceedings which described our work on Adaptive Contextualization (AC). The core theory behind AC is that we can use intelligent user modeling and analysis to combat the emergence of selection bias during visualization-based data selection. By quantifying bias and making it visible through the user interface, users are more informed about the underling quality of their data.
I’m happy to announce that a new article, written collaboratively with co-author David Borland at RENCI, has been accepted for publication in IEEE Computer Graphics and Applications (CG&A). The article, titled “Data-Driven Healthcare: Challenges and Opportunities for Interactive Visualization,” is scheduled to appear in the May/June 2016 issue as part of the “Visualization Viewpoints” series.
In the article, we outline a variety of high-impact opportunities for visualization and visual analytic methods to impact the healthcare domain.
I’m happy to announce that I’ll be part of a panel at the AMIA 2016 Joint Summits on Translational Science together with Zhaohui Cai, Aaron Kamauu, and Gerasimos Petratos. The extended abstract we submitted was accepted to appear in the proceedings, and the panel has been scheduled for March 22, 2016 at 10:30am. For more information about the Joint Summits, click here.
Here is a brief abstract for our planned panel:
I’m excited to announce that my paper about Adaptive Contextualization, written collaboratively with Shun Sun (an MSIS student here at at SILS/UNC-Chapel Hill) and Nan Cao (NYU Shanghai), has been accepted for publication at ACM Intelligent User Interfaces (IUI) 2016. What we propose in the paper is a method for using visualization provenance to guard against selection bias during exploratory visualization-based data selection. These capabilities have been added to our Tempo visual cohort selection prototype.