Information Visualization for Health Care
Upcoming SlideShare
Loading in...5
×

Like this? Share it with your network

Share
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
2,297
On Slideshare
2,284
From Embeds
13
Number of Embeds
3

Actions

Shares
Downloads
32
Comments
0
Likes
0

Embeds 13

http://kristw.yellowpigz.com 7
https://www.linkedin.com 5
http://a0.twimg.com 1

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. INFORMATION VISUALIZATIONfor healthcareKrist wongsuphasawat@kristwongzDepartment of Computer Science & Human-Computer Interaction LabUniversity of Maryland
  • 2. INFORMATION VISUALIZATIONINFO. VIS.
  • 3. A picture is worth a thousand words.
  • 4. INFORMATION VISUALIZATIONINFO. 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]!  
  • 5. Anscombe’s quartet #1 #2 #3 #4 X Y X Y X Y X Y 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
  • 6. Anscombe’s quartet #1 #2 #3 #4 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 Identical statistics
  • 7. Anscombe’s quartet #1 #2 #3 #4 12! 10! 14! 14! 9! 10! 12! 12! 8! 7! 10! 10! 8! 6! 8! 8! 6! 5! 4! 6! 6! 4! 3! 4! 4! 2! 2! 2! 2! 1! 0! 0! 0! 0! 0! 5! 10! 15! 0! 5! 10! 15! 0! 5! 10! 15! 0! 10! 20! But very different
  • 8. INFORMATION VISUALIZATION Visual representation + User interactions click, drag, zoom, select, etc.!  
  • 9. HealthcareElectronic 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 Americas medical records are computerized. This will cut waste, eliminate red tape and reduce the need to repeat expensive medical tests. But it just wont 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]!  
  • 10. EMRs + INFO. VIS.A lot of data! Help understand data!    
  • 11. One patient x
  • 12. Example of EMRs System
  • 13. Lifelines One patient x [Plaisant et al. 1998]!http://www.cs.umd.edu/hcil/lifelines!
  • 14. LifeLines
  • 15. LifeLines
  • 16. LifeLines
  • 17. LifeLines
  • 18. LifeLines
  • 19. Lifelines user study Faster decision Better recall
  • 20. Lifelines One patientDemographic-  Gender-  Age-  … x [Plaisant et al. 1998]! Medical Events* -  Emergency room on Jan 15 -  Surgery on Oct 1 -  … http://www.cs.umd.edu/hcil/lifelines!
  • 21. Lifelines 2Multiple patientsxxxxx [Wang et al. 2008, 2009]! http://www.cs.umd.edu/hcil/lifelines2!
  • 22. Case study Contrast-induced nephropathy Radiographic Examination (Medical Imaging) e.g. X-ray using a contrast agent e.g. Iodine, Barium x Creatinine -  Amino Acid Damage to the kidney -  Levels in blood reflect kidney function
  • 23. Data : contrast & creatinine CREAT- Normal level of Creatinine RADIOLOGY Radiographic exam (with contrast) CREAT-H High level of Creatinine (bad) x Time Jan Feb Mar Apr
  • 24. LifeLines2
  • 25. Video demoData Analysis with Lifelines2
  • 26. Lifelines 2Multiple patientsxxxxx [Wang et al. 2008, 2009]! http://www.cs.umd.edu/hcil/lifelines2!
  • 27. Lifelines 2search from medical events xxxxx [Wang et al. 2008, 2009]! http://www.cs.umd.edu/hcil/lifelines2!
  • 28. Data : patients transfer 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
  • 29. Improve the Quality of Care Pa$ent  ID:  45851733     Pa$ent  ID:  45851732    12/02/2008  14:26  Arrival  12/02/2008  14:26I  D:  45851731 Pa$ent   Emergency       Emergency Department 12/02/2008  14:26  Arrival   6,000+12/02/2008  22:44  ICU  mergency   12/02/2008  14:26  E  12/05/2008  05:071Floor  Arrival   12/02/2008     4:26 12/02/2008  22:44  ICU  mergency   12/02/2008  14:26  E12/08/2008  10:02  Floor   12/05/2008  05:07  Floor   12/02/2008  22:44  ICU  12/14/2008  06:19  Discharge   12/08/2008  10:02  Floor   12/05/2008  05:07  Floor     12/14/2008  06:19  Discharge   12/08/2008  10:02  Floor     12/14/2008  06:19  Discharge   patients per month  
  • 30. task Find “Bounce backs” ICU Floor ICU within 2 days Limitations High-level questions Arrival ? ICU ? ?
  • 31. patientsx x x xx x x x xx x xx x x xx x x x x x x
  • 32. LifeFlow overview xx x x xx x x x xx[Wongsuphasawat et al. 2011]! http://www.cs.umd.edu/hcil/lifeflow!
  • 33. Video demo Creating LifeFlow
  • 34. LifeFlow
  • 35. Video demoData Analysis with LifeFlow
  • 36. collected visual representation large eye interactions rich EMRs + INFO. VIS. A lot of data! Help understand data!     and more… Lifelines LifeFlow Save more lives Lifelines 2 Patientslikeme / i2b2 / BTRIS Many case studies / etc. Krist wongsuphasawat @kristwongz kristw@cs.umd.edu! http://www.cs.umd.edu/hcil/temporalviz!  
  • 37. Other examplesVisualizations in the Medical Domain
  • 38. MIDGAARDhFp://www.infovis-­‐wiki.net/index.php?$tle=MIDGAARD  
  • 39. HemoVishFp://people.seas.harvard.edu/~borkin/HemoVis/