AI for Health Care

Yu-Chuan (Jack) Li, M.D., Ph.D., FACMI Professor in Biomedical Informatics,

Dean, College of Medical Science and Technology Taipei Medical University

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

Computer Methods and Programs in Biomedicine

International Journal for Quality in Health Care

Editor-in-Chief

ISQua / OUP Elsevier 2000 submissions, 360 paper published / year

3

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

6

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

Why AIHC in Taiwan • 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

• 100% e-claim since 1995 95% EHR

• Highly accessible exams/tests 2M CT|MR /yr

• Very standard coding and data schema AI in Medicine market value

9.2 Billion USD in 2019

AI與疾病的明日之戰

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

•Prediction → Prevention 輕忽預防

Top 5 Causes of Death

Ref: Medical error—the third leading cause of death in the US, British Medical Journal(BMJ), 2016;353:i2139 doi: 10.1136/bmj.i2139

611 K

585 K

251 K

149 K

41 K

1st Heart Disease

2nd Cancer

3rd Medical Error

4th COPD

5th Suicide

Causes of Death Per Year, USA

98 K 2000

2013

Poor Quality

• 45% did NOT receive recommended care

(US adult)

• Pneumonia 61% X

• Asthma 47% X

• Hypertension 35% X

• 41% did NOT receive standard care (AU

Children)

McGlynn et al., New England Journal of Medicine, 2003

Braithwaite et al., JAMA, 2018

12

One-size-fits-all Medicine

• A clinical practice hinges on “average”

• Lab data range: No difference between

10 y/o girl and 80 y/o man

• Diagnostic accuracy: 20% wrong

• Allergy Info: 40% missing

• Family Hx: 90% incomplete

• Genomic, Behavioral and Environmental

data NOT AVAILABLE to doctors

13

Preventive Medicine is Hard

• No visible target

• Repetitive & Slow

• No pain

• People don’t understand probability

• Science can’t produce reliable/useful

predictions

Low market value

To Prevent Medication Errors

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

15

Results after 2 months of running

A Medical Center in Taiwan

• Patients：72,378

Reminders：2140 (3%)

Agreed：1038

• High risk medications

• Patients ：17,793人

Reminders ：114

Agreed ：62

A Healthcare System in the US

• Patients : 31,728人次

Reminders : 2,723 (8.6%)

*Estimated

17

“Time Matrix” CNN transformation

本團隊榮獲肝病防治基金會 研究獎助金 (2018.08.11)

94% 肝癌預測技術正確性

(AUROC)

Highly Accurate Prediction of Liver Cancer

18

Patient

Profile

Diagnosis

/Problem

Procedures Medication

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

Input Variables for AIHC

Patient Profile

Diagnosis /Problem

Procedures Medication

Lab/Exam

12

9

7

8

5

11

10

4 3

2

Birth

YC (Jack) Li et. al., 2016

Phenotype

(Environmental)

AIHC的八大類資料來源與時間軸

Output Variables of AIHC

Death

YC (Jack) Li et. al., 2016

Clinical Events

Treatment Rehabilitation

Prognosis

Management

Diagnosis

Prediction Early

Detection Suggestion/Recommendation

Augmented

Intelligence

AI and Medicine – for NOW

Adapted from: Charles P. Friedman. J Am Med Inform Assoc. 2009;16:169 –170.

Rule the World!

Men lose jobs

Establishing AIMHI in Taiwan

台灣人工智慧醫療創新研究中心

http://AIMHI.tw

Conclusion

• AI and Healthcare can go hand-in-hand

• AI can help on QPS, one-size-fits-all and prediction/prevention

• AI has to change the future of medicine (or we may not have one)

• because we deserve it!

Thank you for your attention