A Visual Analytics Approach to Comparing Cohorts of Event Sequences - - PowerPoint PPT Presentation

▶
a visual analytics approach to comparing cohorts of event
SMART_READER_LITE
LIVE PREVIEW

A Visual Analytics Approach to Comparing Cohorts of Event Sequences - - PowerPoint PPT Presentation

Feb 25, 2024 135 likes •572 views

A Visual Analytics Approach to Comparing Cohorts of Event Sequences Sana Malik, Fan Du, Megan Monroe, Eberechukwu Onukwugha, Catherine Plaisant, & Ben Shneiderman May 29, 2014 HCIL Symposium Time-stamped event data is widespread

slide-1
SLIDE 1

A Visual Analytics Approach to Comparing Cohorts of Event Sequences 


Sana Malik, Fan Du, Megan Monroe, Eberechukwu Onukwugha, Catherine Plaisant, & Ben Shneiderman

May 29, 2014 — HCIL Symposium

slide-2
SLIDE 2

Time-stamped event data is widespread

slide-3
SLIDE 3

→ E-commerce

Time-stamped event data is widespread

slide-4
SLIDE 4

→ E-commerce → Online education

Time-stamped event data is widespread

slide-5
SLIDE 5

→ E-commerce → Online education → Patient medical histories

Time-stamped event data is widespread

slide-6
SLIDE 6

Often, we compare groups in these datasets

slide-7
SLIDE 7

→ E-commerce

Often, we compare groups in these datasets

slide-8
SLIDE 8

→ E-commerce

Often, we compare groups in these datasets

slide-9
SLIDE 9

Existing tools fall into two categories.

slide-10
SLIDE 10
  • 1. Visual
slide-11
SLIDE 11

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

  • 2. Statistical
slide-12
SLIDE 12

visualization + statistics =CoCo

(Cohort Comparison)

slide-13
SLIDE 13

Visualizing Statistical Results

slide-14
SLIDE 14

Visualizing Statistical Results

slide-15
SLIDE 15

Visualizing Statistical Results

slide-16
SLIDE 16

Visualizing Statistical Results

slide-17
SLIDE 17

Visualizing Statistical Results

slide-18
SLIDE 18

Visualizing Statistical Results

Normal Floor Bed Emergency

slide-19
SLIDE 19

Visualizing Statistical Results

Normal Floor Bed Emergency

slide-20
SLIDE 20

Visualizing Statistical Results

Normal Floor Bed Emergency

slide-21
SLIDE 21

Normal Floor Bed Emergency

Visualizing Statistical Results

slide-22
SLIDE 22

Normal Floor Bed Emergency

Visualizing Statistical Results

slide-23
SLIDE 23

Normal Floor Bed Emergency

Visualizing Statistical Results

slide-24
SLIDE 24

Normal Floor Bed Emergency

Visualizing Statistical Results

(59.8%) (10.1%) Δ = 49.7% (p = 0.001)

slide-25
SLIDE 25

Visualizing Statistical Results

slide-26
SLIDE 26

Visualizing Statistical Results

slide-27
SLIDE 27

Visualizing Statistical Results

slide-28
SLIDE 28

Visualizing Statistical Results

slide-29
SLIDE 29
  • 1. Summary Statistics
slide-30
SLIDE 30
  • 2. Prevalence Metrics

→ Events

slide-31
SLIDE 31
  • 2. Prevalence Metrics

→ Event sequences → Events

slide-32
SLIDE 32
  • 2. Prevalence Metrics

→ Event sequences → Co-occurring (non-sequential) events → Events

slide-33
SLIDE 33
  • 2. Prevalence Metrics

→ Event sequences → Co-occurring (non-sequential) events → Outcomes of records → Events

slide-34
SLIDE 34
  • 3. Time Metrics

→ Absolute times

slide-35
SLIDE 35
  • 3. Time Metrics

→ Absolute times → Duration of events, gaps, and overlaps

slide-36
SLIDE 36
  • 4. Attribute Metrics

→ Event attributes

Emergency | Doctor = Smith

slide-37
SLIDE 37
  • 4. Attribute Metrics

→ Event attributes → Record attributes

slide-38
SLIDE 38

Pilot User Study

→ 10 participants → Used CoCo & side-by-side EventFlow for analysis → Counted the number of insights users made

slide-39
SLIDE 39

Pilot User Study

slide-40
SLIDE 40

Pilot User Study

“I like the fact that you give me two different tools. I

can look at the data in different ways”

!

→ 9/10 participants started with EventFlow for an “overview” → Used CoCo to confirm findings

slide-41
SLIDE 41

Future Work

→ Support a variety of statistical tests → Add visualizations of distributions → Support exploration and search of sequences

slide-42
SLIDE 42

For more information & to become a CoCo user: web www.cs.umd.edu/hcil/coco email maliks@cs.umd.edu

! ! !

Supported by Oracle and the University of Maryland Center for Health-related Informatics and Bioimaging (CHIB), a unit of UMIACS

A Visual Analytics Approach to Comparing Cohorts of Event Sequences 


Sana Malik, Fan Du, Megan Monroe, Eberechukwu Onukwugha, Catherine Plaisant & Ben Shneiderman