Comparing Cohorts of Event Sequences presented by Sana Malik with Fan Du, Catherine Plaisant, and Ben Shneiderman May 26, 2016 — HCIL 33 rd Annual Symposium, College Park A VISUAL ANALYTICS APPROACH
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Comparing Cohorts of Event Sequences
presented by Sana Malik
with Fan Du, Catherine Plaisant, and Ben Shneiderman
May 26, 2016 — HCIL 33rd Annual Symposium, College Park
A VISUAL ANALYTICS APPROACH
often, analysts compare cohorts within datasets
any groups
of users,
patients,
or records
often, analysts compare cohorts within datasets
?
FREQUENT PATTERNS
ABSENCE OF EVENTS
DURATION
StatisticsCohort SelectionData Collection
StatisticsVisual AnalyticsCohort SelectionData Collection
Cohort Selection StatisticsData Collection Visual Analytics
Monroe et al. Temporal event sequence simplification. IEEE Transactions on Visualization and Computer Graphics (TVCG 2013).
EVENTFLOW
Monroe et al. Temporal event sequence simplification. IEEE Transactions on Visualization and Computer Graphics (TVCG 2013).
EVENTFLOW
Monroe et al. Temporal event sequence simplification. IEEE Transactions on Visualization and Computer Graphics (TVCG 2013).
EVENTFLOW
?
Cohort Selection StatisticsData Collection Visual Analytics
Aspirin -> Emergency -> Normal Floor Bed -> ICU -> Exit 38.37, 0.0, 4.11e-123 Emergency -> ICU -> Exit 24.61, 0.0, 2.11e-73 Emergency -> Normal Floor Bed -> Exit -> ICU 5.26, 0.0, 4.12e-15 Emergency -> Normal Floor Bed -> ICU -> Exit 5.26, 0.0, 4.12e-15 Aspirin -> Emergency -> ICU -> Normal Floor Bed -> Exit 7.89, 3.22, 1.20e-06 Aspirin -> Emergency -> ICU -> Intermediate Care -> Exit 4.15, 0.0, 4.22e-12 Emergency -> Normal Floor Bed -> ICU -> Intermediate Care -> Exit 2.97, 0.0, 7.02e-09 Aspirin -> Emergency -> ICU -> Exit 2.80, 0.0, 2.02e-08 Emergency -> Normal Floor Bed -> ICU -> Normal Floor Bed -> Exit 1.44, 0.0, 9.83e-05 Emergency -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> Exit 1.01, 0.0, 0.00 Aspirin -> Emergency -> Normal Floor Bed -> ICU -> Intermediate Care -> Exit 0.76, 0.0, 0.00 Aspirin -> Emergency -> Normal Floor Bed -> Exit 0.0, 1.61, 3.37e-05 Aspirin -> Emergency -> ICU -> Normal Floor Bed -> ICU -> Exit 0.0, 2.37, 2.84e-07 Aspirin -> Emergency -> Normal Floor Bed -> ICU -> Normal Floor Bed -> Exit 3.82, 6.45, 0.00 Emergency -> ICU -> Normal Floor Bed -> ICU -> Exit 0.0, 4.07, 7.17e-12 Emergency -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> Exit 0.0, 4.24, 2.48e-12 Emergency -> Exit 0.0, 11.88, 9.00e-34 Emergency -> ICU -> Normal Floor Bed -> Exit 0.0, 16.12, 2.18e-46 Aspirin -> Emergency -> Exit 0.0, 47.79, 2.49e-162 Aspirin -> Emergency -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> Exit 0.0, 0.59, 0.02 Aspirin -> Emergency -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> Exit Aspirin -> Emergency -> ICU -> Exit 2.80, 0.0, 2.02e-08 Emergency -> Normal Floor Bed -> ICU -> Normal Floor Bed -> Exit 1.44, 0.0, 9.83e-05 Emergency -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU ->
5.26, 0.0, 4.12e-15 Emergency -> Normal Floor Bed -> ICU -> Exit 5.26, 0.0, 4.12e-15 Aspirin -> Emergency -> ICU -> Normal Floor Bed -> Exit 7.89, 3.22, 1.20e-06 Aspirin -> Emergency -> ICU -> Intermediate Care -> Exit 4.15, 0.0, 4.22e-12 Emergency -> Normal Floor Bed -> ICU -> Intermediate Care -> Exit 2.97, 0.0, 7.02e-09 Aspirin -> Emergency -> ICU -> Exit 2.80, 0.0, 2.02e-08 Emergency -> Normal Floor Bed -> ICU -> Normal Floor Bed -> Exit 1.44, 0.0, 9.83e-05 Emergency -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> Exit 1.01, 0.0, 0.00 Aspirin -> Emergency -> Normal Floor Bed -> ICU -> Intermediate Care -> Exit 0.76, 0.0, 0.00 Aspirin -> Emergency -> Normal Floor Bed -> Exit 0.0, 1.61, 3.37e-05 Aspirin -> Emergency -> ICU -> Normal Floor Bed -> ICU -> Exit 0.0, 2.37, 2.84e-07 Aspirin -> Emergency -> Normal Floor Bed -> ICU -> Normal Floor Bed -> Exit 3.82, 6.45, 0.00 Emergency -> ICU -> Normal Floor Bed -> ICU -> Exit 0.0, 4.07, 7.17e-12 Emergency -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> Exit 0.0, 4.24, 2.48e-12 Emergency -> Exit 0.0, 11.88, 9.00e-34 Emergency -> ICU -> Normal Floor Bed -> Exit 0.0, 16.12, 2.18e-46 Aspirin -> Emergency -> Exit 0.0, 47.79, 2.49e-162 Aspirin -> Emergency -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> Exit 0.0, 0.59, 0.02 Aspirin -> Emergency -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> Exit Aspirin -> Emergency -> ICU -> Exit 2.80, 0.0, 2.02e-08 Emergency -> Normal Floor Bed -> ICU -> Normal Floor Bed -> Exit 1.44, 0.0, 9.83e-05 Emergency -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> ICU -> Normal Floor Bed -> Exit 1.01, 0.0, 0.00 Aspirin -> Emergency -> Normal Floor Bed -> ICU -> Intermediate Care -> Exit 0.76, 0.0, 0.00 Aspirin -> Emergency -> Normal Floor Bed -> Exit 0.0, 1.61, 3.37e-05
SAS Business Analytics Software. Vers. 9.4. SAS Institute, 2014. Computer software.
StataCorp. 2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP.
SAS STATA
StatisticsVisual AnalyticsCohort SelectionData Collection
Cohort SelectionData Collection
Statistics
Visual Analytics
HIGH-VOLUMEHypothesis Testing
HIGH-VOLUMEHypothesis Testing
OF RESULTSSystematic Exploration
HIGH-VOLUMEHypothesis Testing
OF RESULTSSystematic Exploration
REAL-WORLDCase Study
HIGH-VOLUMEHypothesis Testing
Emergency Room
Normal Floor Bed
ICU
Discharged
start and end of record
non-consecutive (contains other events between)
non-consecutive (contains other events between)
1 SHORT SEQUENCE
14 UNIQUE PATTERNS
FREQUENCYOn average, does this sequence occur more frequently per record in one cohort than the other?
DURATIONOn average, does this sequence take longer in one cohort than the other?
RECORD COVERAGEDoes this sequence occur in more records in one cohort than the other?
FREQUENCYOn average, does this sequence occur more frequently per user in one cohort than the other?
DURATIONOn average, does this sequence take longer in one cohort than the other?
RECORD COVERAGEDoes this sequence occur in more records in one cohort than the other? 14 UNIQUE PATTERNS
X 3 METRICS
42 HYPOTHESES
HIGH-VOLUMEHypothesis Testing
OF RESULTSSystematic Exploration
REAL-WORLDCase Study
OF RESULTSSystematic Exploration
Demo
HIGH-VOLUMEHypothesis Testing
OF RESULTSSystematic Exploration
REAL-WORLDCase Study
REAL-WORLDCase Study
MULTI-DIMENSIONAL IN-DEPTH LONG-TERM CASE STUDIES (MILCS)
Entry Interview & Training (1 session)
Exit Interview (1 session)
Partners Use Tool
Partners Provide Feedback
Researchers Refine Tool
(3 months)
Papers, insights, discoveriesDemonstrate utility, refine tool
For Researchers For Partners
B. Shneiderman and C. Plaisant. Strategies for evaluating information visualization tools: Multi- dimensional in-depth long-term case studies. In BELIV ’06: Proceedings of the 2006 AVI workshop on BEyond time and errors, pages 1–7. ACM, 2006.
CASE STUDY PARTNERS
CASE STUDY PARTNERS
Users’ events on a product website • viewing the display ads • signing up for promotions or free trials • purchasing products
PARTICIPANTS & DATASET
Three analysts at Adobe • One experienced user • Two novice users
Dataset Size • 6,999 users • 124 events types / 81,563 events
Compare users who purchased a product with using trials versus without using trials
to understand ad-related behaviors
GOAL
SYSTEM USE
SYSTEM USE
“Event filtering was the most helpful to focus the analysis”
SYSTEM USE
“Reduced metric calculation time provided a much better user experience for data analysis”
SYSTEM USE
Users who had a trial viewed display ads more than the other group & contained more retargeting events. Analysts hypothesized trial users were “explorers” and non-trial users were “experienced users” based on event pattern differences
RESULTS: FOR PARTNERS
HIGH-VOLUMEHypothesis Testing
OF RESULTSSystematic Exploration
REAL-WORLDCase Study
Future Work
• Extensions to other data types (e.g., networks)
• Interval events
• Cohort selection
presented by Sana Malik
email [email protected]
www http://hcil.umd.edu/coco
Support from Adobe, Oracle, and the University of Maryland’s Center for Health-related Informatics & Bioimaging (CHIB).
Comparing Cohorts of Event Sequences
A VISUAL ANALYTICS APPROACH
TRY COCO!
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