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[台灣人工智慧學校] 開創台灣產業智慧轉型的新契機

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台北總校第四期開學典禮主題演講
李友專院長 (臺北醫學大學醫學科技學院)

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[台灣人工智慧學校] 開創台灣產業智慧轉型的新契機

  1. 1. AI for Earlier and Safer Medicine Yu-Chuan (Jack) Li, M.D., Ph.D., FACMI Professor in Biomedical Informatics, Dean, College of Medical Science and Technology Taipei Medical University
  2. 2. A bit about myself • Professor in Biomedical Informatics • Board-certified Dermatologist • Elected Fellow, ACMI (American College of Medical Informatics) and IAHIS (International Academy of Health Information Science) • Fellow, ACHI (Australian College of Health Informatics) • Editor-in-Chief, Computer Methods and Programs in Biomedicine (IF 2.7) • Editor-in-Chief, International Journal for Quality in Healthcare (IF 2.6) http://Jackli.cc
  3. 3. Computer Methods and Programs in Biomedicine International Journal for Quality in Health Care Editor-in-Chief ISQua / OUPElsevier 2000 submissions, 360 paper published / year 3
  4. 4. Defining AI Artificial intelligence (AI) is intelligence exhibited by machines. … Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". … "We don't need Artificial Intelligence if we don't have Natural Stupidity!" - Professor Allan T. Pryor
  5. 5. Author of the book - “Health Care Future with AI”
  6. 6. 7 Evolution of AI • 1960 Age of Reasoning • Logic-based • heuristic search • 1990 Age of Representation • Rule-based • Knowledge engineering • Expert system • 2015~ Age of Machine Learning • Big Data-driven • Autonomous learning • 2045 Age of Superintelligence?
  7. 7. 8 Why AI in HC • Taiwan has a strong ICT industry/academia • Taiwan has one of the most“high performance”healthcare system in the world • Very high outpatient visit – 15 visits/pers/yr • Diagnoses/Drugs coded by physicians, NOT coders • Accurate e-prescription – $$$ by NHI x 200 • 23 million people for 23 years EHR • Very standard coding and data schema
  8. 8. Dimensions of AI in Health Care • Stakeholders • Locality • Urgency • Business/sustainability model • AI Technology (supervised, unsupervised learning…etc.) • Data source/quality • ELSI (Ethical, Legal and Social Implications)
  9. 9. Three-Dimension Model • 4 Stakeholders – Patient/Consumer, Clinician, Administrator, Payer/Insurer • 4 Locality – GP office, Hospital, Home, Other institution • 3 Urgency types – Preventive care, Acute care, Long-term care 10
  10. 10. Preventive Care AcuteCare Long-term Care Preventive Care AcuteCare Long-term Care Preventive Care AcuteCare Long-term Care Preventive Care AcuteCare Long-term Care GP Office 22 6 5 23 7 6 10 3 4 17 6 7 Hospital 15 28 5 22 29 9 17 20 7 15 15 4 Home 30 7 17 17 3 9 6 2 5 14 2 8 Other Institutions 7 4 4 4 1 3 2 2 2 3 0 5 Locality Stakeholders Urgency BeforeDiscussion Patient /Consumer Physician Administrator Payer /Insurer Three-Dimension Table (4x4x3)
  11. 11. Key Issues in Current Health Care •Medical Errors 醫療錯誤 •Poor/Inconsistent Quality 品質不佳 •One-size-fits-all Approach 以偏概全 •Prevention ignored 輕忽預防
  12. 12. 13 Prevention is Hard • No visible target • Repetitive & Slow • No pain • People don’t understand probability • Science can’t produce useful predictions  Low market value
  13. 13. 14 Predictions are Useless Unless they are: •Accurate •Timely •Individualized •Actionable
  14. 14. The Levels of Prevention AI-based Earlier Detection AI-based Earlier Intervention AI-based Earlier Risk Reduction
  15. 15. MoleMe Project • To determine whether the risk of a mole is high enough to justify a visit to the doctors • 3,000 patients; 4 dermatologists • Images + 5 simple variables • Machine learning, supervised • ResNet -> AUROC of 90%
  16. 16. Earlier detection of moles MoleMe MoleMe
  17. 17. AESOP Project • AI-Enhanced Safety of Prescription • 700M prescriptions; 1.3B Diagnoses; 2.5B medications • 16,000 variables; 400 doctors • Machine learning, unsupervised • 75% to 90% agreement by experts reviewers
  18. 18. 733.4 Millions Prescriptions 80 Million Dx-Med and 2.25 Million Med-Med Associations Explored Medication codes Mapped to 1,500 unique WHO codes Diagnoses ICD codes 20,000 unique ICD codes Machine Learning Learn from Doctors‘ Behavior 1.34B 2.53B Prevent Medication Errors at the Earliest
  19. 19. 20
  20. 20. Preliminary Results A Medical Center in Taiwan • Patients:72,378 Reminders:2140 (3%) Agreed:1038 (48%) • High risk medications • Patients :17,793人 Reminders :114 Agreed :62 (54%) A Healthcare System in the US • Patients : 31,728 Reminders : 2,723 (8.6%) *Estimated
  21. 21. Temporal Cancer Prediction • To determine the occurrence of cancer in the next 12 months based on the previous 36 months of PHR • 80K liver cancer patients; 320K control; 2,700 variables • Machine learning, supervised • Turn Time-matrix into images • AUROC of 94%
  22. 22. 23 “Time Matrix” CNN transformation
  23. 23. 本團隊榮獲肝病防治基金會 研究獎助金 (2018.08.11) 94% Accuracy (AUROC) Accurate Prediction of Liver Cancer 24
  24. 24. Patient Profile Diagnosis /Problem ProceduresMedication Lab/Exam 7 11 Age, sex, allergy, weight, height, blood type, body temperature, …etc. YC (Jack) Li et. al., 2004 Current and/or chronic dz, DM, H/T, Pregnancy…etc. Surgery, transfusion, endoscopy, angiogram, PTCA, rehabilitation…etc. Propanolol vs theophylline, Cipro vs aminophylline, Acetaminophen vs Phenytoin…etc. CBC, D/C, Chem- 20, hCG, PT, APTT, INR…etc. e.g. Coumadin vs INR e.g. Wafarin vs angiogram e.g. Penicillin vs PCN allergy e.g. Retinoids vs pregnancy Data Interaction Model for Adverse Event detection 2x 2x 2x 2x 1x
  25. 25. Input Variables for AIHC with the Temporal Dimension Patient Profile Diagnosis /Problem ProceduresMedication Lab/Exam 12 9 7 8 5 11 10 4 3 2 Birth YC (Jack) Li et. al., 2016 Phenotype (Environmental)
  26. 26. Output Variables of AIHC Death YC (Jack) Li et. al., 2016 Treatment Rehabilitation Prognosis Management Diagnosis Prediction Early Detection Suggestion/Recommendation
  27. 27. Conclusion • AI and Healthcare should go hand-in- hand • AI is opening a whole new page of preventive & earlier medicine • AI has to change the future of medicine (or we may not have one) •  because we deserve it!
  28. 28. Thank you for your attention

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