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Big Data, Artificial Intelligence & Healthcare

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Presentation at the 2019 Y4IT Conference, 26 Sept 2019. UP Diliman.

Publicado en: Atención sanitaria
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Big Data, Artificial Intelligence & Healthcare

  1. 1. BIG DATA, ARTIFICIAL INTELLIGENCE & HEALTHCARE Iris Thiele Isip Tan MD, MSc Professor 3, UP College of Medicine Chief, UP Medical Informatics Unit Director, UP Manila Interactive Learning Center
  2. 2. NOTHING TO DISCLOSE I give consent for the audience to tweet this talk and give me feedback (@endocrine_witch). Feel free take pictures of my slides (though the deck will be at www.slideshare.net/isiptan).
  3. 3. BIG (social media) DATA Use of AI in diabetes Will AI replace physicians?
  4. 4. Merchant RA et al. doi.org/10.1371/journal.pone.0215476 Can we predict individuals’ medical diagnoses from language posted on social media? Can we identify specific markers of disease from social media posts? SOCIAL MEDIA + EMR
  5. 5. Merchant RA et al. doi.org/10.1371/journal.pone.0215476
  6. 6. Facebook alone Demographics alone Demographics and Facebook Medical Condition Prediction Strength Merchant RA et al. doi.org/10.1371/journal.pone.0215476 Diabetes!!
  7. 7. All 21 medical condition categories were predictable from Facebook language beyond chance. Medical Condition Prediction Strength Merchant RA et al. doi.org/10.1371/journal.pone.0215476 18 categories better predicted by demographics + Facebook language vs demographics. 10 categories better predicted by Facebook language vs demographics.
  8. 8. Merchant RA et al. doi.org/10.1371/journal.pone.0215476
  9. 9. Merchant RA et al. doi.org/10.1371/journal.pone.0215476
  10. 10. Merchant RA et al. doi.org/10.1371/journal.pone.0215476
  11. 11. Merchant RA et al. doi.org/10.1371/journal.pone.0215476
  12. 12. Privacy Informed Consent Data Ownership Merchant RA et al. doi.org/10.1371/journal.pone.0215476
  13. 13. Predictive associations of language with disease may vary across populations Merchant RA et al. doi.org/10.1371/journal.pone.0215476
  14. 14. Use of AI in diabetes Will AI replace physicians? BIG (social media) DATA
  15. 15. How to calculate insulin bolus in type 1 diabetes
  16. 16. https://mysugr.com/mysugr-bolus-calculator/ Insulin on board Blood glucose Carbs Previous injections Carbs/insulin ratio Insulin correction factor Blood glucose target MySugr Bolus Calculator
  17. 17. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42. Advanced Bolus Calculator for Diabetes (ABC4D) CBR approach: tuning of ISF and CIR for a small set of meal scenarios ISF and CIR from the most similar case used in a standard bolus calculator to suggest a bolus dose No temporal approach
  18. 18. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42. Case-based reasoning model for T1DM bolus insulin advice
  19. 19. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42. CASE FEATURES Determine which parameters are required by bolus calculators Carbohydrate intake Pre-meal blood glucose Target blood glucose level Insulin-on-board Exercise Time Insulin Sensitivity Factor (ISF) Carbohydrate-to-Insulin Ratio (CIR)
  20. 20. RETRIEVE Use the date/time of event to infer ISF and CIR Factors in preceding bolus doses REUSE Adaptation rule which resolves differences between insulin-on- board (IOB) in the problem and retrieved case(s) Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
  21. 21. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42. REUSE step: Average bolus prediction of retrieved cases then adapt Equation for averaging bolus prediction of retrieved cases k = number of retrieved cases in = bolus solution provided by a retrieved case
  22. 22. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42. REUSE step: Average bolus prediction of retrieved cases then adapt Equations for adapting bolus suggestion
  23. 23. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42. REVISE If postprandial BG is equal or close to target BG then recommendation is optimal and not revised
  24. 24. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42. Focus on helping patient directly (instead of aiding the clinician) RETAINS all successful cases Derives bolus suggestion from similar cases
  25. 25. Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42. CBR method can be adopted by insulin pumps, blood glucose monitors, PCs and as a web service
  26. 26. CBR service in the cloud opens possibility of case sharing between subjects Brown D et al. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support. Artificial Intelligence in Medicine 2018;85:28-42.
  27. 27. Use of AI in diabetes Will AI replace physicians? BIG (social media) DATA
  28. 28. Machine learning represents a shifting clinical paradigm from rigidly defined management strategies to data-driven precision medicine. Buch et al. Diabet Med 2018;35:495-7.
  29. 29. Buch et al. Diabet Med 2018;35:495-7. Clinical guidelines will be delivered through apps rather than static documents.
  30. 30. Buch et al. Diabet Med 2018;35:495-7. Healthcare professionals will require adequate training to operate AI-based solutions Appreciate the limitations of technology Over-reliance on AI risks de-skilling the profession
  31. 31. “The pinnacle of AI is being fully autonomous. But I don’t think that will happen in medicine; AI will always need human backup. - Eric Topol MD
  32. 32. A robot may not injure a human being or, through inaction, allow a human being to come to harm. A robot must obey orders given it by human beings except where such orders would conflict with the First Law. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. Isaac Asimov’s Three Laws of Robotics

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