INFORMATION VISUALIZATION
@kristwongz
for healthcare
Krist wongsuphasawat
Department of Computer Science & Human-Computer Interaction Lab University of Maryland
INFORMATION VISUALIZATION INFO. VIS.
A picture is worth a thousand words.
INFORMATION VISUALIZATION INFO. VIS.
Using visual representations and interaction techniques, which take advantage of the human eye’s broad bandwidth pathway into the mind, to allow users to see, explore, and understand large amounts of information at once.”
“ [Wikipedia]!
Anscombe’s quartet
X Y
10.0 8.04
8.0 6.95
13.0 7.58
9.0 8.81
11.0 8.33
14.0 9.96
6.0 7.24
4.0 4.26
12.0 10.84
7.0 4.82
5.0 5.68
X Y
10.0 9.14
8.0 8.14
13.0 8.74
9.0 8.77
11.0 9.26
14.0 8.10
6.0 6.13
4.0 3.10
12.0 9.13
7.0 7.26
5.0 4.74
X Y
10.0 7.46
8.0 6.77
13.0 12.74
9.0 7.11
11.0 7.81
14.0 8.84
6.0 6.08
4.0 5.39
12.0 8.15
7.0 6.42
5.0 5.73
X Y
8.0 6.58
8.0 5.76
8.0 7.71
8.0 8.84
8.0 8.47
8.0 7.04
8.0 5.25
19.0 12.50
8.0 5.56
8.0 7.91
8.0 6.89
#1 #2 #3 #4
Anscombe’s quartet
Property Value
Mean of X 11.0
Variance of X 10.0
Mean of Y 7.5
Variance of Y 3.75
Correlation between X and Y 0.816
Linear regression y = 3.0 +0.5x
#1 #2 #3 #4
Identical statistics
Anscombe’s quartet
#1 #2 #3 #4
0!
2!
4!
6!
8!
10!
12!
0! 5! 10! 15!0!1!2!3!4!5!6!7!8!9!
10!
0! 5! 10! 15!0!
2!
4!
6!
8!
10!
12!
14!
0! 5! 10! 15!0!
2!
4!
6!
8!
10!
12!
14!
0! 10! 20!
But very different
INFORMATION VISUALIZATION
Visual representation
User interactions +
click, drag, zoom, select, etc.!
Healthcare Electronic medical records (EMRs)
To improve the quality of our health care while lowering its cost, we will make the immediate investments necessary to ensure that, within five years, all of America's medical records are computerized. This will cut waste, eliminate red tape and reduce the need to repeat expensive medical tests. But it just won't save billions of dollars and thousands of jobs; it will save lives by reducing the deadly but preventable medical errors that pervade our health-care system.”
“
[President Barack Obama – Jan 2009]!
EMRs A lot of data!
INFO. VIS. + Help understand data!
One patient
x
Example of EMRs System
One patient
x Lifelines
[Plaisant et al. 1998]!http://www.cs.umd.edu/hcil/lifelines!
LifeLines
LifeLines
LifeLines
LifeLines
LifeLines
Faster decision Better recall
Lifelines user study
x One patient
[Plaisant et al. 1998]!http://www.cs.umd.edu/hcil/lifelines!
Lifelines
Demographic - Gender - Age - …
Medical Events* - Emergency room on Jan 15 - Surgery on Oct 1 - …
Multiple patients
x Lifelines 2
[Wang et al. 2008, 2009]!http://www.cs.umd.edu/hcil/lifelines2!
x x x x
Contrast-induced nephropathy
Case study
x Radiographic Examination (Medical Imaging) e.g. X-ray using a contrast agent e.g. Iodine, Barium
Damage to the kidney
Creatinine - Amino Acid - Levels in blood reflect kidney function
CREAT- Normal level of Creatinine RADIOLOGY Radiographic exam (with contrast) CREAT-H High level of Creatinine (bad)
Data : contrast & creatinine
x Time
Jan Feb Mar Apr
LifeLines2
Video demo Data Analysis with Lifelines2
Multiple patients
x [Wang et al. 2008, 2009]!
http://www.cs.umd.edu/hcil/lifelines2!
x x x x Lifelines 2
search from medical events
x Lifelines 2
[Wang et al. 2008, 2009]!http://www.cs.umd.edu/hcil/lifelines2!
x x x x
ARRIVAL Arrive the hospital EMERGENCY Emergency room ICU Intensive Care Unit FLOOR Normal room EXIT-ALIVE Leave the hospital alive EXIT-DEAD Leave the hospital dead
Data : patients transfer
Improve the Quality of Care
Emergency Department Pa$ent ID: 45851733
12/02/2008 14:26 Arrival 12/02/2008 14:26 Emergency 12/02/2008 22:44 ICU 12/05/2008 05:07 Floor 12/08/2008 10:02 Floor 12/14/2008 06:19 Discharge
Pa$ent ID: 45851732 12/02/2008 14:26 Arrival 12/02/2008 14:26 Emergency 12/02/2008 22:44 ICU 12/05/2008 05:07 Floor 12/08/2008 10:02 Floor 12/14/2008 06:19 Discharge
Pa$ent ID: 45851731 12/02/2008 14:26 Arrival 12/02/2008 14:26 Emergency 12/02/2008 22:44 ICU 12/05/2008 05:07 Floor 12/08/2008 10:02 Floor 12/14/2008 06:19 Discharge
6,000+ patients per month
Find “Bounce backs”
within 2 days
ICU Floor ICU
task
High-level questions Arrival
ICU
?
? ?
Limitations
x x x x x x x x x x x x x x x x x x x
x x x x
patients
overview
x
LifeFlow
[Wongsuphasawat et al. 2011]!http://www.cs.umd.edu/hcil/lifeflow!
x x x x x x x x x x
Video demo Creating LifeFlow
LifeFlow
Video demo Data Analysis with LifeFlow
INFO. VIS. EMRs + A lot of data!
Help understand data!
rich
collected large eye
visual representation interactions
Save more lives
@kristwongz Krist wongsuphasawat
[email protected]!http://www.cs.umd.edu/hcil/temporalviz!
LifeFlow Lifelines
Lifelines 2
and more…
Patientslikeme / i2b2 / BTRIS Many case studies / etc.
Other examples Visualizations in the Medical Domain
MIDGAARD hFp://www.infovis-‐wiki.net/index.php?$tle=MIDGAARD
HemoVis hFp://people.seas.harvard.edu/~borkin/HemoVis/
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