Aligning Temporal Data by Sentinel Events: Discovering Patterns in Electronic Health Records

1. Aligning Temporal Data by Sentinel Events:Discovering Patterns in Electronic Health Records Taowei David Wang, Catherine Plaisant, Alexander J. Quinn, Roman Stanchak, Ben Shneiderman, and Shawn Murphy Presented By: Ben Harner 05/05/2009

2. 2 Authors: Taowei David Wang University of Maryland: Human Computer Interaction Lab Ph. D. Student

3. 3 Authors: Catherine Plaisant Associate Director of Research of the Human-Computer Interaction Lab of the University of Maryland Doctorat d'Ingenieur degree in France in 1982 Enjoys working with multidisciplinary teams on designing and evaluating new interface technologies that are useable and useful

4. 4 Authors: Alexander J. Quinn University of Maryland: Human Computer Interaction Lab Ph. D. Student Research Include human-computer interaction (HCI), digital libraries, distributed human computation (DHC)

5. 5 Authors: Roman Stanchak University of Maryland Currently on a leave of absence from the PhD program Interests include Computer Vision, Machine Learning, Graph Analysis, Data Mining, Information Visualization, Human Computer Interaction, Numerical Analysis

6. 6 Authors: Ben Shneiderman Professor in the Department of Computer Science at the University of Maryland Founding Director (1983-2000) of the Human-Computer Interaction Laboratory Member of the Institute for Advanced Computer Studies

7. 7 Authors: Shawn Murphy The Associate Director of the Laboratory of Computer Science at Massachusetts General Hospital Assistant Professor of Neurology at Harvard Medical School.

8. 8 Introduction Discovering patterns is a common step of scientific inquiry Medical researchers are interested in temporal patterns across health records Explore strategies to support tasks that involve temporal comparisons relative to important events Heart attacks, strokes, etc �

9. 9 Related Research Timelines to present temporal data TimeSearcher, TimeSearcher2, VizTree Visualization work on single patient records Powsner, Tufte Visualizing categorical data on timelines Lexis diagrams, Lifelines Form-based query interface for specifying temporal data PatternFinder

10. 10 Lifelines2 Lifelines2 is an interactive visualization tool for visualizing temporal categorical data across multiple records. The goal of the project is to enable discovery and exploration of patterns across these records to support hypothesis generation, and finding cause-and-effect relationships in a population. These tasks are not� specific to the medical domain, but first motivation was by EHRs.

11. 11 Lifelines2 Lifelines2 Demo

12. 12 Controlled Experiment Procedure Scholastic records for a set of students Data was point events like submitting a paper, software release, dates of dissertation proposal or defense, signing up for a class, or submitting a job application 20 Participants Divided into two independent parts

13. 13 Controlled Experiment Procedure: Part 1 Participants asked to perform tasks regarding events around first occurrence of event using 2 interfaces Alignment as an operator No alignment Tasks to complete How many students submitted a paper within 1 month after proposal? (5 records) How many students submitted a paper within 1 month after proposal? (20 records) How many students published at least 3 papers between proposal and defense? What occurred most often within a month of a student�s 1st paper submission?

14. 14 Controlled Experiment Procedure: Part 2 Only used the interface variation with alignment, but varies how the events were represented The first condition the lines of the interval of validity were visible The second condition the lines of interval validity were not visible Tasks to complete Assuming a class lasts 3 months, how many students proposed while they were taking a class? Assuming a class lasts 3 months, and it takes 2 months to prepare for proposal, how many students were preparing for proposal while taking a class?

15. 15 Controlled Experiment Intervals of validity

16. 16 Controlled Experiment Results Task Time Competition Error Rate by Tasks

17. 17 Domain Expert Qualitative Evaluation Procedure 4 medical professionals 11 patients who had asthma, and were prescribed some type of steroid Some of the patients had other conditions that might also require a steroid prescription Participants were asked to select patients for a study who had been given steroids for their asthma condition and nothing else.

18. 18 Domain Expert Qualitative Evaluation Results Three out of the four participants had no problem interpreting the visualization They were able to immediately figure out the timeline, each patient�s record, and the temporal event data shown as triangles The important result was that the displayed intervals affect how the participants viewed and interpreted the data

19. 19 Conclusions Lifelines2 provides operations to align, rank, and filter the results of queries Displays of patient histories aligned on events enable medical researchers to spot precursor, co-occurring, and aftereffect events.

20. 20 Discussion Importance Credibility Novelty Applicability Generalizability Scalability Assumptions Readability

21. 21 Questions for the Authors? If you think of any additional questions please e-mail me at: benjamin-harner@uiowa.edu