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OUTFLOW

Krist Wongsuphasawat David H. Gotz

Visualizing Patients Flow by Symptoms & Outcome

IBM T.J. Watson Research Center

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Congestive Heart Failure (CHF)

Electronic Medical Records

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Patient #1

Time

Aug 1998 Ankle Edema

Jan 1999 Weight Loss

Oct 1998 Cardiomegaly

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Ankle

Patient #1

Cardio. Weight

Ankle

Patient #2

Cardio. Rales

Time

Ankle

Patient #3

Cardio. Rales

Ankle

Patient #n

Cardio. Rales Weight

Many patient records

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with outcome

Ankle

Patient #1

Cardio. Weight

Ankle

Patient #2

Cardio. Rales

Time

Ankle

Patient #3

Cardio. Rales

Ankle

Patient #n

Cardio. Rales Weight

Die (0)

Live (1)

Live (1)

Live (1)

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information overload!

6,000 patients

6,000,000 medications 200,000 symptoms

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consumable

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Overview / Summary

Millions of records

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Steps 1.  Aggregation

2.  Visual Encoding

3.  Interactions

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Step 1: Aggregation Patients Outflow graph

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Patient #1

Patient #2

Patient #4

Patient #3

Patient #5

Patient #6

Patient #n

Patient #7

…

Outflow Graph

Patient records

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Assumption •  Symptoms are accumulative.

Ankle

Patient #1

Cardio. Weight

Patient #1

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Assumption •  Symptoms are accumulative.

Ankle

Patient #1

Cardio. Weight

Ankle

Patient #1

Ankle Ankle

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Assumption •  Symptoms are accumulative.

Ankle

Patient #1

Cardio. Weight

Ankle

Patient #1

Ankle Cardio.

Ankle Cardio.

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Assumption •  Symptoms are accumulative.

Ankle

Patient #1

Cardio. Weight

Ankle

Patient #1

Ankle Cardio.

Ankle Cardio. Weight

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Assumption •  Symptoms are accumulative.

Ankle

Patient #1

Cardio. Weight

Ankle [A]

Patient #1

Ankle Cardio. [A,C]

Ankle Cardio. Weight

[A,C,W]

State

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Select alignment point Target patient’s current state

Ankle Cardio. Weight

[A,C,W]

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Filter patients

[A]

Patient #1

[A,C] [A,C,W]

[A]

Patient #2

[A,W] [A,R,W]

[A]

Patient #3

[A,W] [A,C,W]

[A,C,R,W]

[A,C,R,W]

[A,C,D,W]

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Select alignment point Target patient’s current state

What are the paths that led to ?

What are the paths after ?

Ankle Cardio. Weight

[A,C,W]

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Outflow Graph

[A,C,W]

[A,C]

[A,C,D,W]

[A]

[ ]

Alignment Point

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Outflow Graph

[A,C,W]

[A,C]

[A,W]

[A,C,D,W]

[A]

[ ]

Alignment Point

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Outflow Graph

[A,C,W]

[A,C]

[A,W]

[A,C,R,W]

[A,C,D,W]

[A]

[ ]

Alignment Point

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Outflow Graph

[A,C,W]

[A,C]

[A,W]

[C,W]

[A,C,R,W]

[A,C,D,W]

[A]

[C]

[W]

[ ]

Alignment Point

Average outcome = 0.4 Average time = 10 days Number of patients = 10

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Step 2: Visual Encoding Outflow graph Outflow visualization

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NOW Future Past

A!C!

A!

C!

A!C!W!

A!C!D!

Color is outcome measure.

Node’s height is number of patients.

Time edge’s width is duration of transition.

Node’s horizontal position shows sequence of states.

time edge

link edge

End of path

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Step 3: Interactions Static vis. Interactive vis.

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Interactions •  Panning

•  Zooming

•  Brushing + Freezing

•  Tooltip

•  Highlight target

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Sample Analysis What can we learn from it?

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Analysis Demo •  outflow_analysis_demo.mp4

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Steps 1.  Aggregation –  Outflow graph

2.  Visual Encoding –  Sketch

–  Visualization

3.  Interactions –  Details on demand

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Future Work •  Evaluation & Design Improvement

•  Use outcome from predictive modeling

•  Similarity measure to select similar patients

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Conclusions •  Electronic Medical Records

–  Rich information

–  Large

•  Visualization: Outflow –  Visual summary: overview

–  Interactive exploration: zoom, filter and details

•  Not specific to CHF, or medical domain

Contact me krist.wongz@gmail.com @kristwongz

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Soccer Results

2-1

2-0

1-1

0-2

2-2

3-1

1-0

0-1

0-0

Alignment Point

Average outcome = win/lose Average time = 10 minutes Number of games = 10

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Acknowledgement •  Charalambos (Harry) Stavropoulos

•  Robert Sorrentino

•  Jimeng Sun

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Conclusions •  Electronic Medical Records

–  Rich information

–  Large

•  Visualization: Outflow –  Visual summary: overview

–  Interactive exploration: zoom, filter and details

•  Not specific to CHF, or medical domain

Contact me krist.wongz@gmail.com @kristwongz

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