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Outflow: Visualizing Patients Flow by Symptoms & Outcome

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OUTFLOW
 Visualizing Patients Flow
 by Symptoms & Outcome
  Krist Wongsuphasawat
  David H. Gotz
  IBM T.J. Watson Researc...
Electronic Medical Records
Congestive Heart Failure
                     (CHF)




                             m
Time


Patient #1


    Aug 1998      Oct 1998          Jan 1999
   Ankle Edema   Cardiomegaly       Weight Loss




     ...

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Outflow: Visualizing Patients Flow by Symptoms & Outcome

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Paper presentation at the Workshop on Visual Analytics in Healthcare in conjunction with the IEEE VisWeek 2011, Providence, RI, 2011.

Abstract:
Electronic Medical Record (EMR) databases contain a large amount of temporal events
such as diagnosis dates for various symptoms.
Analyzing disease progression pathways in terms of these observed events
can provide important insights into how diseases evolve over time.
Moreover, connecting these pathways to the eventual outcomes of the corresponding patients
can help clinicians understand how certain progression paths may lead to better or worse outcomes.
In this paper, we describe the Outflow visualization technique,
designed to summarize temporal event data that has been extracted from the EMRs of a cohort of patients.
We include sample analyses to show examples of the insights that can be learned from this visualization.

Paper presentation at the Workshop on Visual Analytics in Healthcare in conjunction with the IEEE VisWeek 2011, Providence, RI, 2011.

Abstract:
Electronic Medical Record (EMR) databases contain a large amount of temporal events
such as diagnosis dates for various symptoms.
Analyzing disease progression pathways in terms of these observed events
can provide important insights into how diseases evolve over time.
Moreover, connecting these pathways to the eventual outcomes of the corresponding patients
can help clinicians understand how certain progression paths may lead to better or worse outcomes.
In this paper, we describe the Outflow visualization technique,
designed to summarize temporal event data that has been extracted from the EMRs of a cohort of patients.
We include sample analyses to show examples of the insights that can be learned from this visualization.

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Outflow: Visualizing Patients Flow by Symptoms & Outcome

  1. 1. OUTFLOW Visualizing Patients Flow by Symptoms & Outcome Krist Wongsuphasawat David H. Gotz IBM T.J. Watson Research Center mm
  2. 2. Electronic Medical Records Congestive Heart Failure (CHF) m
  3. 3. Time Patient #1 Aug 1998 Oct 1998 Jan 1999 Ankle Edema Cardiomegaly Weight Loss m
  4. 4. Many patient records Time Patient #1 Ankle Cardio. Weight Patient #2 Ankle Cardio. Rales Patient #3 Ankle Rales Cardio. Patient #n Ankle Weight Rales Cardio. m
  5. 5. with outcome Time Patient #1 Live (1) Ankle Cardio. Weight Patient #2 Live (1) Ankle Cardio. Rales Patient #3 Die (0) Ankle Rales Cardio. Patient #n Live (1) Ankle Weight Rales Cardio. m
  6. 6. information overload! 6,000 patients 200,000 symptoms 6,000,000 medications m
  7. 7. consumable m
  8. 8. Overview / Summary Millions of records m
  9. 9. Steps 1.  Aggregation 2.  Visual Encoding 3.  Interactions m
  10. 10. m Step 1: Aggregation Patients Outflow graph
  11. 11. Patient records Patient #1 Patient #2 Outflow Graph Patient #3 Patient #4 Patient #5 Patient #6 Patient #7 … Patient #n m
  12. 12. Assumption •  Symptoms are accumulative. Patient #1 Ankle Cardio. Weight Patient #1 m
  13. 13. Assumption •  Symptoms are accumulative. Patient #1 Ankle Cardio. Weight Patient #1 Ankle Ankle Ankle m
  14. 14. Assumption •  Symptoms are accumulative. Patient #1 Ankle Cardio. Weight Patient #1 Ankle Ankle Ankle Cardio. Cardio. m
  15. 15. Assumption •  Symptoms are accumulative. Patient #1 Ankle Cardio. Weight Patient #1 Ankle Ankle Ankle Cardio. Cardio. Weight m
  16. 16. Assumption •  Symptoms are accumulative. Patient #1 Ankle Cardio. Weight Patient #1 Ankle Ankle Ankle [A] Cardio. Cardio. [A,C] Weight [A,C,W] State m
  17. 17. Select alignment point Target patient’s current state Ankle Cardio. Weight [A,C,W] m
  18. 18. Filter patients Patient #1 [A] [A,C] [A,C,W] [A,C,R,W] Patient #2 [A] [A,W] [A,R,W] [A,C,R,W] Patient #3 [A] [A,W] [A,C,W] [A,C,D,W] m
  19. 19. Select alignment point Target patient’s current state What are the paths What are the paths that led to ? after ? Ankle Cardio. Weight [A,C,W] m
  20. 20. Outflow Graph Alignment Point [A] [A,C] [] [A,C,W] [A,C,D,W] m
  21. 21. Outflow Graph Alignment Point [A] [A,C] [] [A,W] [A,C,W] [A,C,D,W] m
  22. 22. Outflow Graph Alignment Point [A] [A,C] [A,C,R,W] [] [A,W] [A,C,W] [A,C,D,W] m
  23. 23. Outflow Graph Alignment Point [A] [A,C] [A,C,R,W] [] [C] [A,W] [A,C,W] [A,C,D,W] [W] [C,W] Average outcome = 0.4 Average time = 10 days Number of patients = 10 m
  24. 24. m Step 2: Visual Encoding Outflow graph Outflow visualization
  25. 25. Past Future NOW Node’s horizontal position shows sequence of states. A! C! W! End of path A! A! C! time link A! Node’s height is edge edge C! number of patients. D! C! Color is outcome Time edge’s width is measure. duration of transition. m
  26. 26. m
  27. 27. m Step 3: Interactions Static vis. Interactive vis.
  28. 28. Interactions •  Panning •  Zooming •  Brushing + Freezing •  Tooltip •  Highlight target m
  29. 29. m Sample Analysis What can we learn from it?
  30. 30. Analysis Demo •  outflow_analysis_demo.mp4 m
  31. 31. Steps 1.  Aggregation –  Outflow graph 2.  Visual Encoding –  Sketch –  Visualization 3.  Interactions –  Details on demand m
  32. 32. Future Work •  Evaluation & Design Improvement •  Use outcome from predictive modeling •  Similarity measure to select similar patients m
  33. 33. 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 m
  34. 34. Soccer Results Alignment Point 1-0 2-0 2-2 0-0 1-1 2-1 3-1 0-1 0-2 Average outcome = win/lose Average time = 10 minutes Number of games = 10 m
  35. 35. Acknowledgement •  Charalambos (Harry) Stavropoulos •  Robert Sorrentino •  Jimeng Sun m
  36. 36. 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 m
  37. 37. m THANK YOU ขอบคุณครับ

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