1. Patternfinder 3.0 : Sparse Temporal Data Visual Query Application Hyunyoung Song, Nathaniel Ayewah, Gleneesha Johnson Department of Computer Science, University of Maryland Spring 2006 CMSC838S Term Project

2. Previous Patternfinder

3. New Patternfinder Doctor’s query examples  Washington National Hospital ED  Visual Query Interface  Visualization Panel

4. Data Model Event 1 Event 2 Value Range Variable Type Time span Relative Change Time Range Event N Window span Time Span Attribute Temporal Entity Event Attribute Cardinality Pattern  Sequences of event  constrained by time span, relative change, etc…

5. 4 Tasks with Incremental Complexity Simple Event Search Relative Change Search Sequence Search Windowed Sequence Search Event 1 Event 2 Value Range Variable Type Time span Relative Change Time Range Event N Window span Time Span Attribute Temporal Entity Event Attribute Cardinality

6. 4 Tasks with Incremental Complexity Simple Event Search Relative Change Search Sequence Search Windowed Sequence Search Event 1 Event 2 Value Range Variable Type Time span Relative Change Time Range Event N Window span Time Span Attribute Temporal Entity Event Attribute Cardinality

7. 4 Tasks with Incremental Complexity Simple Event Search Relative Change Search Sequence Search Windowed Sequence Search Event 1 Event 2 Value Range Variable Type Time span Relative Change Time Range Event N Window span Time Span Attribute Temporal Entity Event Attribute Cardinality

8. 4 Tasks with Incremental Complexity Simple Event Search Relative Change Search Sequence Search Windowed Sequence Search Event 1 Event 2 Value Range Variable Type Time span Relative Change Time Range Event N Window span Time Span Attribute Temporal Entity Event Attribute Cardinality

9. Simple Event Search Task  Find all patients with HGB measurements above 15 Gm/dl Features  Level structured design  Reference levels  Matched Events Visualization

10. Relative Change Search Task  Find all patients with an increase of more than 0.5 K/ul in their white blood cell count  Find all patients with a 10% decrease in their white blood cell count Features  Timespan Constraint  Condensed Overview / Detailed view  Timespan Labels

11. Sequence Search Task  Find all patients with 2 WBC measurements followed by at least 1 RBC measurement within 4 to 12 hours Features  Shape Encoding  Dynamic Filter based on Demography

12. Windowed Sequence Search Task  Find the following measurements: white blood cells followed by red blood cells followed by haemoglobin, within a window of 1 day Features  Window zoom

13. Design Principles Visual Query Formulation  Level Structured Design  Consistent Symbols Visualization  Timeline  Colors and Shapes  Overview and Detail

14. Future Work Deploy as ActiveX User Studies Optimize Query algorithms Streaming or Real-time Data Overlapping Events