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)

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. 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. 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. 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. Author of the book -
“Health Care
Future with AI”

7. 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?

8. 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

9. 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)

10. 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

11. 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)

12. 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

13. 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

14. 14
Predictions are Useless
Unless they are:
•Accurate
•Timely
•Individualized
•Actionable

15. The Levels of Prevention
AI-based Earlier
Detection
AI-based Earlier
Intervention
AI-based Earlier
Risk Reduction

16. 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%

17. Earlier detection of moles
MoleMe
MoleMe

18. 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

19. 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

20. 20

21. 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

22. 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%

23. 23
“Time Matrix” CNN transformation

24. 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

25. 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

26. 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)

27. Output Variables of AIHC
Death
YC (Jack) Li et. al., 2016
Treatment Rehabilitation
Prognosis
Management
Diagnosis
Prediction Early
Detection
Suggestion/Recommendation

28. 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!

29. Thank you for your attention