Comparing Cohorts of Event Sequences A VISUAL ANALYTICS APPROACH - - PowerPoint PPT Presentation

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Comparing Cohorts of Event Sequences A VISUAL ANALYTICS APPROACH - - PowerPoint PPT Presentation

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Comparing Cohorts of Event Sequences A VISUAL ANALYTICS APPROACH presented by Sana Malik with Fan Du, Catherine Plaisant, and Ben Shneiderman May 26, 2016 HCIL 33 rd Annual Symposium, College Park often, analysts compare cohorts within

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Comparing Cohorts

  • f 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

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  • ften, analysts compare cohorts within datasets
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any groups

  • f users,

patients,

  • r records
  • ften, analysts compare cohorts within datasets
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?

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FREQUENT PATTERNS

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ABSENCE OF EVENTS

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DURATION

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Statistics Cohort Selection Data Collection

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Statistics Visual Analytics Cohort Selection Data Collection

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Cohort Selection Statistics Data Collection Visual Analytics

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Monroe et al. Temporal event sequence simplification. IEEE Transactions on Visualization and Computer Graphics (TVCG 2013).

EVENTFLOW

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Monroe et al. Temporal event sequence simplification. IEEE Transactions on Visualization and Computer Graphics (TVCG 2013).

EVENTFLOW

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Monroe et al. Temporal event sequence simplification. IEEE Transactions on Visualization and Computer Graphics (TVCG 2013).

EVENTFLOW

?

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Cohort Selection Statistics Data Collection Visual Analytics

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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 ->

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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

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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

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Statistics Visual Analytics Cohort Selection Data Collection

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Cohort Selection Data Collection Statistics Visual Analytics

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HIGH-VOLUME

Hypothesis Testing

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HIGH-VOLUME

Hypothesis Testing

OF RESULTS

Systematic Exploration

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HIGH-VOLUME

Hypothesis Testing

OF RESULTS

Systematic Exploration

REAL-WORLD

Case Study

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HIGH-VOLUME

Hypothesis Testing

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Emergency Room

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Normal Floor Bed

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ICU

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Discharged

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start and end of record

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non-consecutive (contains other events between)

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non-consecutive (contains other events between)

1 SHORT SEQUENCE 14 UNIQUE PATTERNS

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FREQUENCY

On average, does this sequence occur more frequently per record in one cohort than the other?

DURATION

On average, does this sequence take longer in one cohort than the other?

RECORD COVERAGE

Does this sequence occur in more records in one cohort than the other?

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FREQUENCY

On average, does this sequence occur more frequently per user in one cohort than the other?

DURATION

On average, does this sequence take longer in one cohort than the other?

RECORD COVERAGE

Does this sequence occur in more records in one cohort than the other?

14 UNIQUE PATTERNS

X 3 METRICS 42 HYPOTHESES

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HIGH-VOLUME

Hypothesis Testing

OF RESULTS

Systematic Exploration

REAL-WORLD

Case Study

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OF RESULTS

Systematic Exploration

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Demo

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HIGH-VOLUME

Hypothesis Testing

OF RESULTS

Systematic Exploration

REAL-WORLD

Case Study

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REAL-WORLD

Case Study

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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, discoveries Demonstrate 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.

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CASE STUDY PARTNERS

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CASE STUDY PARTNERS

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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
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Compare users who purchased a product with using trials versus without using trials to understand ad-related behaviors GOAL

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SYSTEM USE

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SYSTEM USE

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“Event filtering was the most helpful to focus the analysis” SYSTEM USE

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“Reduced metric calculation time provided a much better user experience for data analysis” SYSTEM USE

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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

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HIGH-VOLUME

Hypothesis Testing

OF RESULTS

Systematic Exploration

REAL-WORLD

Case Study

Future Work

  • Extensions to other data

types (e.g., networks)


  • Interval events
  • Cohort selection
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presented by Sana Malik email maliks@cs.umd.edu 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

  • f Event Sequences

A VISUAL ANALYTICS APPROACH

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