The DBMI Open Insights Seminars are occasional research talks related to the mission of the Department of Biomedical Informatics. This includes themes such as:
- Provisioning big data to the scientific community
- Getting the big picture on human health
- Learning from each patient
- Advancing basic science with data science
- Understanding disease beyond heredity: environmental impact
- Instrumenting the health enterprise for discovery and intervention
This interdisciplinary seminar provides an open forum to engage participants in a discussion about the future of quantitative methods and engineering in biomedicine. The seminar features local, national, and international speakers who are leaders in their field and have an interest in engaging with the larger community while visiting DBMI to meet with colleagues.
Selected talks are available on our YouTube channel.
Thursday, February 14 (Countway Library, 4th FL, Room 403) *12-1PM*
Cody Dunne, PhD
Temporal Event Sequence Visualization for Type 1 Diabetes Treatment Decision Support
The modern world is awash in complex data that can contain the keys to improving our lives. The scope of this data has rapidly outpaced our capabilities to analyze and comprehend, so we turn to computers to help. However, state-of-the-art technology can only supplement the human element. People assist in each stage of data science, whether it’s data cleaning, understanding algorithm design, exploring computed results, or collaborating and sharing for decision-making. To present complex information to humans, we use visualizations that leverage our extraordinary perceptual system which can detect trends, clusters, gaps, and outliers almost instantly.
My talk will focus on the specific problem of temporal event sequence visualization for treating type 1 diabetes. Type 1 diabetes is a chronic, incurable autoimmune disease affecting millions of Americans. Treatment requires frequent adjustments to insulin protocol, diet, and behavior in collaboration with a clinician. Manual logs and medical device data are collected by patients, but these multiple sources of data are presented in disparate visualization designs to the clinician—making temporal inference difficult. These issues are compounded when there is poor data quality such as missing data, erroneous data, or uncertainty in values or timestamps.
I will discuss a data and task abstraction for this problem using a novel hierarchical task abstraction approach. I will also demonstrate the interactive visualization tool we developed in this design study: IDMVis. IDMVis includes a novel technique for folding and aligning records by dual sentinel events and scaling the intermediate timeline. Using IDMVis, clinicians were able to identify issues of data quality such as missing or conflicting data, reconstruct patient records when data is missing, differentiate between days with different patterns, and promote educational interventions after identifying discrepancies.