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Artificial Intelligence and Diabetes

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Symposium presentation at the 2019 annual convention of the Philippine Society of Endocrinology, Diabetes & Metabolism

Published in: Health & Medicine
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Artificial Intelligence and Diabetes

  1. 1. ARTIFICIAL INTELLIGENCE AND DIABETES 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. What is AI? Use of AI in diabetes Will AI replace physicians?
  4. 4. ARTIFICIAL INTELLIGENCE Allow machines to sense, reason, act and adapt like humans do - or in ways beyond our abilities https://newsroom.intel.com/news/many-ways-define-artificial-intelligence/#gs.0msuwc
  5. 5. More computing power More data Better algorithms Broad investment AI is not new … https://newsroom.intel.com/news/many-ways-define-artificial-intelligence/#gs.0msuwc
  6. 6. https://newsroom.intel.com/news/many-ways-define-artificial-intelligence/#gs.0msuwc
  7. 7. https://newsroom.intel.com/news/many-ways-define-artificial-intelligence/#gs.0msuwc
  8. 8. https://newsroom.intel.com/news/many-ways-define-artificial-intelligence/#gs.0msuwc
  9. 9. What is AI? Use of AI in diabetes Will AI replace physicians?
  10. 10. How to calculate insulin bolus in type 1 diabetes
  11. 11. https://www.uni-trier.de/index.php?id=44502&L=2 CASE-BASED REASONING Problem-solving paradigm in artificial intelligence Similar problems tend to have similar solutions Case base: collection of memorized chunks of experience (cases) Adaptive systems: new problem solving experience is retained and outdated experience is removed
  12. 12. 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
  13. 13. 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)
  14. 14. 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
  15. 15. 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.
  16. 16. 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
  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. REUSE step: Average bolus prediction of retrieved cases then adapt Equations for adapting bolus suggestion
  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. REVISE If postprandial BG is equal or close to target BG then recommendation is optimal and not revised
  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. 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
  20. 20. 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
  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. CBR method can be adopted by insulin pumps, blood glucose monitors, PCs and as a web service
  22. 22. 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.
  23. 23. OBJECTIVE Apply gradient forest analysis to identify subgroups of ACCORD participants with increased/decreased risk of all- cause mortality attributable to intensive therapy
  24. 24. Action to Control Cardiovascular Risk in Diabetes (ACCORD) Trial halted due to increase in all-cause mortality in intensive therapy arm Median A1c Intensive: 6.4% Standard: 7.5% ACCORD study group. N Engl J Med 2008; 358:2545-2559
  25. 25. Which subgroup in ACCORD was most likely to benefit or have increased mortality? Heterogeneous treatment effects
  26. 26. Basu et al. Diabetes Care 2017 doi.org/10.2337/dc17-2252
  27. 27. Basu et al. Diabetes Care 2017 doi.org/10.2337/dc17-2252 Summary risk stratification decision tree Hemoglobin Glycosylation Index (Observed - Predicted A1c) Predicted A1c 0.009 x FPG [mg/dl] + 6.8
  28. 28. Basu et al. Diabetes Care 2017 doi.org/10.2337/dc17-2252 Survival curves for all-cause mortality among subsets identified by each subgroup in the decision tree A: HGI <0.44, BMI <30 kg/m2 and age <61 years B: HGI <0.44, BMI <30 kg/m2 and age >61 years
  29. 29. Basu et al. Diabetes Care 2017 doi.org/10.2337/dc17-2252 Survival curves for all-cause mortality among subsets identified by each subgroup in the decision tree C: HGI <0.44 and BMI >30 kg/m2 D: HGI >0.44
  30. 30. What is AI? Use of AI in diabetes Will AI replace physicians?
  31. 31. 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.
  32. 32. Buch et al. Diabet Med 2018;35:495-7. Clinical guidelines will be delivered through apps rather than static documents.
  33. 33. 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
  34. 34. “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
  35. 35. 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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