f inding patterns in temporal data
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FINDING PATTERNS IN TEMPORAL DATAKRIST WONGSUPHASAWATTAOWEI DAVID WANGCATHERINE PLAISANTBEN SHNEIDERMAN
HUMAN-COMPUTER INTERACTION LAB UNIVERSITY OF MARYLAND
27th HCIL Symposium
May 27, 2010
FINDING PATTERNS IN TEMPORAL DATAKRIST WONGSUPHASAWATTAOWEI DAVID WANGCATHERINE PLAISANTBEN SHNEIDERMAN
HUMAN-COMPUTER INTERACTION LAB UNIVERSITY OF MARYLAND
27th HCIL Symposium
May 27, 2010
Patient ID: 4585173712/02/2008 14:26 Arrival12/02/2008 14:36 Emergency12/02/2008 22:44 ICU12/05/2008 05:07 Floor12/14/2008 06:19 Exit
Time
EmergencyICU
FloorExit
TEMPORAL CATEGORICAL DATA• A type of time series
04/26/2010 10:00 31.0304/26/2010 10:15 31.0104/26/2010 10:30 31.0204/26/2010 10:45 31.0804/26/2010 11:00 31.16
EventCategory
Stock: MicrosoftNumerical
Arrival
Event
TEMPORAL CATEGORICAL DATA
Electronic Health Records: symptoms, treatment, lab testTraffic incident logs: arrival/departure time of each unitStudent records: course, paper, proposal, defense, etc.
Others: web logs, usability study logs, etc.
10+ years work on temporal visualization(mostly on Electronic Health Records)
LIFELINES
SINGLE RECORD
[Plaisant et al. 1998]http://www.cs.umd.edu/hcil/lifelines
LifeLines – Single Patient
working with physicians at WASHINGTON HOSPITAL CENTER
EXAMPLE DATA• Patient transfers
ARRIVAL Arrive the hospitalEMERGENCY Emergency roomICU Intensive Care UnitINTERMEDIATE Intermediate Medical
CareFLOOR Normal roomEXIT-ALIVE Leave the hospital aliveEXIT-DEAD Leave the hospital dead
TASKS
within 2 days
ICU Floor ICU
• Example: Finding “Bounce backs”
LIFELINES 2
RECORDRECORDRECORD
RECORD
RECORD
[Wang et al. 2008, 2009]http://www.cs.umd.edu/hcil/lifelines2
LifeLines2 – Search and Visualize
ARF (Align-Rank-Filter)Framework
Temporal Summary
Multiple Records
ALIGNMENT• Sentinel events as reference points
Time
Patient #45851737 ArrivalEmergency
ICUFloor
ExitPatient #43244997 Arrival
EmergencyICU
FloorExit
June July August
ALIGNMENT (2)• Time shifting
Time
Patient #45851737 AdmitEmergency
ICUFloor
ExitPatient #43244997 Admit
EmergencyICU
FloorExit
0 1 M 2 M
SIMILAN
RECORDRECORDRECORD
RECORD
RECORD
[Wongsuphasawat & Shneiderman 2009]http://www.cs.umd.edu/hcil/similan
Similan – Search by Similarity
Similan – Search by Similarity
FINDING “BOUNCE BACKS”Before After
• Much faster to specify new query• Visualizing the results gives better
understanding
USER STUDIES: SEARCH
ExactMUST have A, B, C
Record#1
Record#2
Record#3
moresimilar
Similarity-basedSHOULD have A, B, C
SimilanLifeLines2
Query
Record#2
Record#1
Record#3
Query
USER STUDIES: SEARCH
ExactMUST have A, B, C
Similarity-basedSHOULD have A, B, C
Query
Record#1
Record#2
Record#3
moresimilar
Query
Record#2
Record#1
Record#3
SimilanLifeLines2
1
NEW STUFFNeeds for an overview -> LifeFlow!
TASKS
within 2 days
ICU Floor ICU
• Example: Finding “Bounce backs”
• Other questionsArrival
ICU
?
??
LIFEFLOW
RECORD
RECORD
RECORD
RECORD
RECORD
RECORD
RECORD
RECORD
AGGREGATEMerge multiple records into
tree
VISUALIZEDisplay the aggregation
AGGREGATE• Aggregate by prefix
#1
#2#3
#4
Example with 4 records
AGGREGATE• Aggregate by prefix
#1
#2#3
#4
VISUALIZE• Inspired by the Icicle tree [Fekete 2004]
Number of files
VISUALIZE (2)• Use horizontal axis to represent time• Video
DEMO – LIFEFLOW When the lines are combined into flow
FUTURE WORK• Comparison
Jan-Mar 2008 April-June 2008
IntermediateICUIntermediateICU
Floor
TAKE-AWAY MESSAGEInformation visualization is a powerful way to explore temporal patterns.
You can work with us on new case studies.
TEMPORAL CATEGORICAL DATA
Electronic Health Records: symptoms, treatment, lab testTraffic incident logs: arrival/departure time of each unitStudent records: course, paper, proposal, defense, etc.
Others: web logs, usability study logs, etc.
EXAMPLE – TRAFFIC INCIDENTS
ACKNOWLEDGEMENTDR. PHUONG HO, DR. MARK SMITH, DAVID
ROSEMANWASHINGTON HOSPITAL CENTER
http://www.whcenter.org
NATIONAL INSTITUTES OF HEALTH (NIH) - GRANT CA147489 http://www.nih.gov
MICHAEL PACK, MICHAEL VANDANIKERCENTER FOR ADVANCED TRANPORTATION
TECHNOLOGY LAB(CATT LAB)
http://www.cattlab.umd.edu
TAKE-AWAY MESSAGEInformation visualization is a powerful way to explore temporal patterns.
You can work with us on new case studies.
More demos this afternoon{kristw, tw7, plaisant, ben}@cs.umd.eduhttp://www.cs.umd.edu/hcil/temporalviz
Q&AQuestions?
{kristw, tw7, plaisant, ben}@cs.umd.edu
http://www.cs.umd.edu/hcil/temporalviz
THANK YOUThank you
BACKUP SLIDESJunkyard...
LIFELINES2• 8 case studies
– Bounce backs– Step ups– BIPAP– Etc.
DR. P
Does not help exploring sequential patternsNeeds a new overview
LifeLines2’s Temporal Summary [Wang et al. 2009]
Continuum’s Histogram [Andre 2007]
USER STUDIES• 8 Extensive case studies• Compared LifeLines2 with Similan
– Learn advantages & disadvantages• Drawing is preferred• Clear cut off points is needed
• Working on improvements– Flexible temporal search
SIMILAN• Compared with LifeLines2 in an
experiment– Learn advantages & disadvantages– Drawing is preferred– No clear cut off points
• Working on improvements– Flexible temporal search
LIFEFLOWAGGREGATE
VISUALIZE
Merge multiple records into tree
Display the tree
APPROACHESExact SearchMUST have A, B, C
Similarity-based SearchSHOULD have A, B, C
Query
Record#1
Record#2
Record#3
moresimilar
Query
Record#1
Record#2
Record#3
MOTIVATION
RESEARCH QUESTIONS
RESEARCHQUESTION#1
PRELIM. + PROPOSED WORK CONCLUSION
RESEARCHQUESTION#2
PRELIM. + PROPOSED WORK
EXPECTED CONTRIBUTIONS1. Design of visual representations, user
interfaces and interaction techniques2. Algorithms for flexible temporal
search3. Evaluation results4. Open new directions for exploring
temporal categorical data
NEEDS FOR AN OVERVIEW• We learn
NEEDS Visualize overviewor show summary
Where should I start?
TEMPORAL VISUALIZATIONSBackground and related work
RELATED WORK• Single record
Patient ID: 4585173712/02/2008 14:26 Arrival12/02/2008 14:26 Emergency12/02/2008 22:44 ICU12/05/2008 05:07 Floor12/08/2008 10:02 Floor12/14/2008 06:19 Exit
Visualization
• E.g. LifeLines, MIDGAARD, etc.
RELATED WORK (2)• Multiple records
VisualizationVisualizationVisualizationVisualization
• E.g. LifeLines2, Continuum, ActiviTree, etc.
Patient ID: 4585173712/02/2008 14:26 Arrival12/02/2008 14:26 Emergency12/02/2008 22:44 ICU12/05/2008 05:07 Floor12/08/2008 10:02 Floor12/14/2008 06:19 Exit
Patient ID: 4585173712/02/2008 14:26 Arrival12/02/2008 14:26 Emergency12/02/2008 22:44 ICU12/05/2008 05:07 Floor12/08/2008 10:02 Floor12/14/2008 06:19 Exit
Patient ID: 4585173712/02/2008 14:26 Arrival12/02/2008 14:26 Emergency12/02/2008 22:44 ICU12/05/2008 05:07 Floor12/08/2008 10:02 Floor12/14/2008 06:19 Exit
Patient ID: 4585173712/02/2008 14:26 Arrival12/02/2008 14:26 Emergency12/02/2008 22:44 ICU12/05/2008 05:07 Floor12/08/2008 10:02 Floor12/14/2008 06:19 Exit
More space please....
INFORMATION VISUALIZATION MANTRAOVERVIEW FIRST, ZOOM AND FILTER, THEN DETAILS ON DEMAND
RELATED WORK (3)• Multiple records
Visualization
• E.g. LifeLines2, Continuum
Patient ID: 4585173712/02/2008 14:26 Arrival12/02/2008 14:26 Emergency12/02/2008 22:44 ICU12/05/2008 05:07 Floor12/08/2008 10:02 Floor12/14/2008 06:19 Exit
Patient ID: 4585173712/02/2008 14:26 Arrival12/02/2008 14:26 Emergency12/02/2008 22:44 ICU12/05/2008 05:07 Floor12/08/2008 10:02 Floor12/14/2008 06:19 Exit
Patient ID: 4585173712/02/2008 14:26 Arrival12/02/2008 14:26 Emergency12/02/2008 22:44 ICU12/05/2008 05:07 Floor12/08/2008 10:02 Floor12/14/2008 06:19 Exit
Patient ID: 4585173712/02/2008 14:26 Arrival12/02/2008 14:26 Emergency12/02/2008 22:44 ICU12/05/2008 05:07 Floor12/08/2008 10:02 Floor12/14/2008 06:19 Exit
SEQUENTIAL PATTERNS
within 2 days
ICU Floor ICU
• Examples: “Bounce backs”
Patient #1
Patient #2
Patient #3
Patient #4
DESIGN AN OVERVIEW• Sequential patterns• Scalability vs. Loss of information
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