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LifeFlow : visualizing an overview of event sequences

LifeFlow : visualizing an overview of event sequences. Krist Wongsuphasawat John Alexis Guerra Gomez Catherine Plaisant Taowei David Wang Ben Shneiderman Meirav Taieb-Maimon Presented by Ren Bauer. Outline. Motivation Related Work Shortcomings Visualization Techniques Evaluation

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LifeFlow : visualizing an overview of event sequences

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  1. LifeFlow: visualizing an overview of event sequences KristWongsuphasawat John Alexis Guerra Gomez Catherine Plaisant Taowei David Wang Ben Shneiderman MeiravTaieb-Maimon Presented by Ren Bauer

  2. Outline • Motivation • Related Work • Shortcomings • Visualization Techniques • Evaluation • Case Studies • User Study

  3. Motivating Case Study • Washington Hospital Center • Dr. Phuong Ho • Bounce Backs • Anomalous Patient Transfer Patterns • Previously viewed sequences in an MS Excel spreadsheet • Needed a more efficient option

  4. Related Work • Temporal • Timelines • Spirals • Hierarchical • Trees • Icicle Plots

  5. Related Work • Temporal Data Visualization

  6. Related Work • Hierarchical Data Visualization

  7. What is LifeFlow? • Developed at the University of Maryland • Data mining tool focused on providing an overview of events • Scales to any number of records • Summarizes all possible sequences • Highlights temporal spacing of events within sequences

  8. Visualization Techniques • Input records • Form timelines • Combine common events • Form LifeFlow Representation

  9. Visualization Techniques

  10. Visualization Techniques

  11. Visualization Techniques

  12. Evaluation • Case Study 1: Medical Domain • One dataset included 7,041 patients • ER patients from Jan 2010 • Most Common: Arrival->ER->Discharge-Alive • 4,591 (65.20%) • 193 (2.74%) Patients LWBS, 38 (0.54%) AWOL • Can be compared with hospital standard for quality control

  13. Evaluation • Case Study 1: Medical Domain • Interesting Patterns • Arrival->ER->Floor->IMC/ICU • “Step up” • Went from floor to ICU more quickly then floor to IMC • Captured screenshots to compare with standards • Found 6 patients experiencing “bounce backs” • Anomalous sequences • Patients being accepted into the ICU after being pronounced dead…

  14. Evaluation • Case Study 1: Medical Domain • Measuring Transfer Time • Easy to make queries such as: “If patients went to the ICU, what was the average transfer time from the ER to the ICU?” • Comparison • Hypothesis about IMC patients being transferred more quickly based on time of day • Found no significant difference

  15. Evaluation • Case Study 2: Transportation Domain • 8 Traffic Response Agencies at U Maryland • Noticed many incidents lasting 24 hours • 12:30am Apr 10th to 11:45pm Apr 10th • Probable data entry error • Ranked agencies based on performance • Fastest (Agency C) 5 minutes • Immediate Clearances • slowest (Agency G) 2 hours 27 minutes • Actually ranked fairly well for “incident”

  16. Evaluation • User Study • 10 Grad students examining 91 medical records • 12 minute training video • 15 simple to complex tasks • “Where did patients usually go after they arrived” • “Retrieve IDs of all patients with this transfer pattern” • Most tasks performed in under 20 seconds • Final Task: 10 minutes to find 3 anomalies intentionally put in data • All students found first 2, most saw third but weren’t sure it was anomalous

  17. Conclusion • Motivation • Need an efficient tool to compare sets of sequences • Previous work insufficient • Solution • LifeFlowvisualization suite • Evaluation • Case studies show usefulness • User study shows usability

  18. Discussion • Some of this information could be found with methods as simple as SQL searches, is this technology really necessary? • What kind of information could it not help us find? • Traffic agencies were ‘ranked’ based on response time, but further investigation revealed these rankings may not mean anything, what are the dangers of technology such as this?

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