CURRENT POSITION

Distinguished Professor
College of Medical Science and Technology
​Taipei Medical University
Dermatologist
Department of Dermatology
Taipei Medical University - Wanfang Hospital
Editor-in-Chief
BMJ Health & Care Informatics journal (the official journal of FCI and published by BMJ)
President-Elect / President
IMIA plays a major global role in the application of information science and technology in the fields of healthcare and research in medical, health and bio-informatics.

EDUCATION

M.D.
Medicine

Taipei Medical University
​1984-1991

Ph.D
​Medical Informatics

University of Utah School of Medicine
​1991-1994

EXPERTISE

AWARDS

TALK

TEDxTaipei

TEDxTaipei 2019 年會：Come．Unity 我，我們
TEDxTaipei
Taiwan | 2019.10.06

AI for the Future of Healthcare

Global Virtual Healthcare Summit 2020
Webinar | 2020.09.04

AI AND SMART HEALTHCARE

BioASIA 2020
 Taiwan Bio Industry Organization
Taiwan | 2020.07.24

DATA MINING LARGE INTERNET NETWORKS TO PREDICT COVID-19 HOTSPOTS

​Data Mining Large Internet Networks to Predict COVID-19 Hotspots
Harvard Global Health Institute, Novartis Foundation and partners
Webinar | 2020.05.12

AI FOR SAFER AND EARLIER MEDICINE

QS-APPLE 2019
QS Quacquarelli Symonds
Japan | 2019.11.27

AI for Earlier and Safer Medicine

RESI Taipei
RESI
Taiwan | 2019.11.14

PUBLICATION

​How Artificial Intelligence can make medicine more pre-emptive?

Artificial Intelligence (AI) is ushering in a new era of “Earlier Medicine”. The burgeoning healthcare technological landscape is showing great potential; from diagnosis and prescribing automation to early detection of disease through efficient and cost-effective patient data screening which benefit from the predictive capabilities of AI. Monitoring prognosis of both in and outpatients has proven a task AI can perform to a reliable degree, saving lives of critical care patients and helping manage cases based upon early predictions.​

“Earlier Medicine” refers to a temporally predictive and proactive approach to individualized health enabled by innovative AI modeling plus longitudinal/personal health big data. It calls on medical practice to not just react or manage present situations but also medical events of the foreseeable future. Such an approach will save money, lives, and our health care ecosystem from inefficiencies and disintegration.

​Full Text
Usman Iqbal, Leo Anthony Celi, Yu-Chuan Jack Li, Journal of Medical Internet Research

​Evaluation of Appropriateness of Overridden Alerts in Computerized Physician Order Entry: A Systematic Review

Clinical decision support system (CDSS) has been appeared as an indispensable tool for reducing medication errors and adverse drug events. A significant amount of studies reported that medication-related alerts in the current CDSS were often inappropriately overridden. Increased overridden rate has already raised questions about the appropriateness of CDSS application and raised a concern about the patient’s safety and quality of care. We, therefore, aim to conduct a systematic review to determine the overridden rate, the reasons for the alerts override and evaluate the appropriateness of overrides reasons.

A total of 23 articles were included in our systematic review. The rage of average override alerts was 46.2% to 96.2%. However, the rage of the overall appropriateness rate for override was 29.4%~100%. Although, the rage of appropriateness was varied by alert types (Drug-allergy: 63.4%~100%, drug-drug interaction: 0%~95%, dose: 43.9%~88.8%, geriatric:14.3%~57%, renal: 27%~87.5%. Indeed, the interrater reliability for the assessment of override alerts appropriateness was excellent (the range of κ was 0.79~0.97). The most common reasons for override were “will monitor”, “patients have tolerated before”.

The findings of our study show that CDSS overridden rate was often high and most of the cases overrides were identified as appropriate but the rate of appropriateness varied widely by different types of alerts. However, inappropriate overrides were associated with a higher rate of potential ADEs when compared with appropriate overrides. Future efforts should try to focus on reducing alert fatigue by providing alerts that are clinically relevant, information related alerts are clear and unambiguous.
​
Full Text
TN Poly, MM Islam, HC Yang, YC Li, JMIR Medical Informatics

​Assessment of Deep Learning Using Nonimaging Information and Sequential Medical Records to Develop a Prediction Model for Nonmelanoma Skin Cancer

A prediction model for new-onset nonmelanoma skin cancer could enhance prevention measures, but few patient data–driven tools exist for more accurate prediction. To use machine learning to develop a prediction model for incident nonmelanoma skin cancer based on large-scale, multidimensional, nonimaging medical information.

This study used a database comprising 2 million randomly sampled patients from the Taiwan National Health Insurance Research Database from January 1, 1999, to December 31, 2013. A total of 1829 patients with nonmelanoma skin cancer as their first diagnosed cancer and 7665 random controls without cancer were included in the analysis. A convolutional neural network, a deep learning approach, was used to develop a risk prediction model. This risk prediction model used 3-year clinical diagnostic information, medical records, and temporal-sequential information to predict the skin cancer risk of a given patient within the next year. Stepwise feature selection was also performed to investigate important and determining factors of the model. Statistical analysis was performed from November 1, 2016, to October 31, 2018.

The findings of this study suggest that a risk prediction model may have potential predictive factors for nonmelanoma skin cancer. This model may help health care professionals target high-risk populations for more intensive skin cancer preventive methods.
​
Full Text
HH Wang, YH Wang, CW Liang, YC Li, JAMA Dermatol, 2019;155(11):1277-1283

A novel tool for visualizing chronic kidney disease associated polymorbidity: a 13-year cohort study in Taiwan

The aim of this study is to analyze and visualize the polymorbidity associated with chronic kidney disease (CKD). The study shows diseases associated with CKD before and after CKD diagnosis in a time-evolutionary type visualization.

Our sample data came from a population of one million individuals randomly selected from the Taiwan National Health Insurance Database, 1998 to 2011. From this group, those patients diagnosed with CKD were included in the analysis. We selected 11 of the most common diseases associated with CKD before its diagnosis and followed them until their death or up to 2011. We used a Sankey-style diagram, which quantifies and visualizes the transition between pre- and post-CKD states with various lines and widths. The line represents groups and the width of a line represents the number of patients transferred from one state to another.

The patients were grouped according to their states: that is, diagnoses, hemodialysis/transplantation procedures, and events such as death. A Sankey diagram with basic zooming and planning functions was developed that temporally and qualitatively depicts they had amid change of comorbidities occurred in pre- and post-CKD states.

​Full Text
CW Huang, S Syed-Abdul, WS Jian, U Iqbal, PA Nguyen, PS Lee, SH Lin, WD Hsu, MSWu, CF Wang, KL Ma, YC Li, Journal of the American Medical Informatics Association, Vol 22, Iss 2, Mar 2015, P290–298

A probabilistic model for reducing medication errors: A sensitivity analysis using Electronic Health Records data

Medication-related clinical decision support systems have already been considered as a sophisticated method to improve healthcare quality, however, its importance has not been fully recognized. This paper's aim was to validate an existing probabilistic model that can automatically identify medication errors by performing a sensitivity analysis from electronic medical record data.

We first built a knowledge base that consisted of 2.22 million disease-medication (DM) and 0.78 million medication-medication (MM) associations using Taiwan Health and Welfare data science claims data between January 1st, 2009 and December 31st, 2011. Further, we collected 0.6 million outpatient visit prescriptions from six departments across five different medical centers/hospitals. Afterward, we employed the data to our AESOP model and validated it using a sensitivity analysis of 11 various thresholds (α = [0.5; 1.5]) that were used to identify positive DM and MM associations. We randomly selected 2400 randomly prescriptions and compared them to the gold standard of 18 physicians' manual review for appropriateness.

One hundred twenty-one results of 2,400 prescriptions with various thresholds were tested by the AESOP model. Validation against the gold standard showed a high accuracy (over 80%), sensitivity (80-96%), and positive predictive value (over 85%). The negative predictive values ranged from 45 to 75% across three departments, cardiology, neurology, and ophthalmology.

​Full Text

CY Huang, PA Nguyen, HCYang, MM Islam, CW Liang, FP Lee, YC Li, Computer Methods and Programs in Biomedicine, Vol 170, Mar 2019, P31-38

​Facebook use leads to health-care reform in Taiwan

​Social networking services are transforming the delivery of health care. Facebook, for example, is now commonly used by medical students, patients, and other stakeholders in the health-care system.1, 2 We describe how Facebook enabled collaboration between stakeholders in emergency-medicine policy in Taiwan, which led to reforms.

The Taiwan Society of Emergency Medicine3 has been in slow-moving negotiation with the Department of Health for the past several years over an appropriate solution to emergency-room overcrowding. A turning point was reached on Feb 8, 2011, when an emergency physician who was an active social network user and popular blogger among the emergency-room staff created a Facebook group called “Rescue the emergency room”. Within a week about 1500 people—most of the emergency department staff around Taiwan—became members of this group and started discussing actively and sharing their experiences. One of the members then posted the group's concerns and problems on the Facebook profile of the Taiwanese Minister of Health. This caused the minister to join the group and get engaged in the discussion. A multiparty dialogue involving many different stakeholders and perspectives was suddenly possible.

​Full Text
S Syed-Abdul, CW Lin, J Scholl, L Fernandez-Luque, WS Jian, MH Hsu, DM Liou, YC Li, The Lancet, Vol 377, Iss 9783, 18–24 June 2011, P2083-2084

BOOKS

AI醫療大未來

作者：李友專
出版社：好人出版
出版時間：2018
ISBN：9789-8692-7513-2

健康醫療資訊科技
​發展政策建言

作者：李友專等人
出版社：國家衛生研究院
出版時間：2018
ISBN：9789-8605-5133-4

建立學習型健康系統之大數據基礎

作者：李友專等人
出版社：國家衛生研究院
出版時間：2018
ISBN：9789-8605-6237-8

醫學資訊管理學
​3版

作者：張慧朗、邱文達、李友專等
出版社：華杏出版股份有限公司
出版時間：2018
ISBN：9789-8619-4503-3

醫學資訊管理學
​2版

作者：張慧朗、邱文達、李友專等
出版社：華杏出版股份有限公司
出版時間：2013
ISBN：9789-8619-4293-3

醫療資訊管理概論

作者：龐婷文, 李友專等
出版社：華格那出版有限公司
出版時間：2010
ISBN：9789-8663-3562-4

醫學資訊管理學

作者：張慧朗、邱文達、李友專等
出版社：華杏出版股份有限公司
出版時間：2006
ISBN：9861-9400-06

國際醫院評鑑
​JCI實戰經驗分享

副總編校：李友專等
出版社：華杏出版股份有限公司
出版時間：2006
ISBN：9572-9228-15

病人安全-理論與實務

主編：邱文達, 李友專等
​出版社：台灣醫務管理學會
出版時間：2004
​ISBN：9570-1725-09

醫療資訊管理學

作者：吳昭新, 李友專等
出版社：偉華書局有限公司
出版時間：2002
ISBN：9576-4050-68

簡明醫學資訊學

總審閱：李友專
出版社：合記圖書出版社
出版時間：2002
​ISBN：9576-6688-75

PATENT

[ Intelligent Personalize Medication Reminding System ]

＃I499995
Wen Shan Jian, Yu Chuan Li, Wei Jung Chang, Hsien Chang Li

[ Ultrasonic and Ozone Cleaning Device Without Water Sink ]

＃M444228
Wen Shan Jian, Yu Chuan Li, Wei Jung Chang

[ Mobile Personal Electronic Health Record System ]

＃M409487
Shabbir-Syed Abdul, Wen-Shan Jian, Yu-Chuan Li ​

NEWS

​全台用藥分析！醫師揭用藥安全的五十道陰影

你的用藥觀念正確嗎？響應9月17日「世界病人安全日」，台北醫學大學李友專教授與醫守科技共同發表全台灣用藥安全分析，呼籲民眾重視自己與家人的用藥安全，尤其是長輩以及常見的降血壓藥和降血糖藥...more

​北醫李友專：AI開創「早覺醫療」新時代 登上《JMIR》

近(11)日，由臺北醫學大學公衛學院、健康資訊科技國際研究中心的團隊，聯合麻省理工學院(MIT)的研究員，在醫學資訊頂尖期刊《JMIR》中發表論文，提出以人工智慧(AI)開創「早覺醫療」(Earlier Medicine)新時代的概念，該由醫學資訊研究所教授李友專領導......more

​

​Dr. Yu-Chuan Li on post-pandemic era: the application of AI in precision diagnosis and treatment

​While some countries are now facing the second wave of COVID-19, many are heading into the post-pandemic era by lifting the lockdown restrictions. Dr. Yu-Chuan Li, the Distinguished Professor at the Graduate Institute of Biomedical Informatics of Taipei Medical University believes that AI medicine can be “the rapidest test of the rapid-test”, before effective medicine and vaccine being introduced. AI technology allows medical workers to identify and quarantine high-risk infectious groups at early stage, and helps reduce the fear and anxiety of society caused by the pandemic........more

​​後肺炎時代更智慧化 AI讓醫療零距離?!
​肺炎促成醫療變革 "分級醫療"新顯學?!​

非凡新聞 錢線百分百 ｜May 28, 2020
​
◎後疫情時代更智慧化 AI讓醫療零距離?!
◎疫情促成醫療變革 "分級醫療"新顯學?!​......more

​​美企對復甦速度悲觀 第二波肺炎危機可避免?!
​美肺炎死亡逾10萬人 防疫準備不足釀禍?!

非凡新聞 錢線百分百 ｜May 28, 2020

◎美肺炎死亡逾10萬人 防疫準備不足釀禍?!
◎專家:第二波疫情可避免! 如何有效防堵?.…more

​突破WHO封鎖！ 台AI權威當「主席」

中天新聞　新神秘52區 ｜May 24, 2020
台灣屢屢被大陸打壓，排除在WHO之外，但是有一個人，卻在去年突破重圍，而且WHO旗下的這個正式組織，還以過半數，把中國大陸除名，台灣甚至以40票對4票，贏得下屆的主席寶座，台灣這位傳奇人物是誰？怎麼辦到的？

....…more

​​癌症不是一天造成的！身體這 6 種症狀持續1~2個月，可能是癌症徵兆​

Heho健康網　盧映慈 ｜May 08,2020
​癌症已經蟬聯37年國人十大死因的榜首，台灣每年有超過4.8萬人因為癌症而死亡，每年新增加的癌症人數，更超過10萬人。在得到癌症的時候，很多人的第一個反應都是「怎麼會是我」？.…more

​​台北醫學大學醫學資訊研究所特聘教授李友專 AI醫療科技防疫先鋒​

能力雜誌　葉小慧 ｜May 05,2020
​新冠肺炎(COVID-19) 全球大流行，戴口罩、社交距離、在家上班等措施，徹底顛覆過去的日常生活想像，更多人思索著：「疫情過後，還能一切回到從前嗎？」而研究AI 醫療近30 年的李友專，則提供了一個醫療大未來的想像。.…more

​​Taiwan dermatologists create Facebook page for foreigners seeking medical help

The China Post ｜Apr 27,2020
​With the coronavirus pandemic taking a toll on frontline medical workers worldwide, many patients with pre-existing medical conditions are struggling to seek medical help.…more

​​武漢肺炎》李友專：疫苗至少還要等1年，但科技能做4件事降恐慌​

未來城市　劉子寧｜Apr 23,2020
​2019年底起自武漢的一聲咳嗽，不只咳去了16萬條珍貴的生命，也咳掉了我們對正常生活的勇氣；而連帶飄出的飛沫，甚至蒸發全世界6%GDP，帶領全人類迎向1930 年代大蕭條後最慘澹的一年。…more

關於癌症，你願花大錢治療，還是花小錢預防？

未來城市 李友專｜Apr 20,2020
​現今AI與醫療的應用，大多是使用於影像辨識；但比起發展醫療影像判讀，將AI應用於「疾病預防」可能來得更重要。
尤其，台灣擁有兩大醫療優勢：健保資料庫與長年累計的電子病歷醫療大數據。若能妥善的運用這些資料，將能擴大AI預防醫學的應用層面。…more

​​Taiwan Can Help！台皮膚科醫師發起「線上諮詢」 全球防疫零距離

健康2.0　王家瑜｜Apr 15,2020
​新冠肺炎（COVID-19）疫情蔓延全球，不少國家紛紛對國民祭出「居家禁足令」，或是呼籲民眾減少非必要的外出。在這樣全球醫護人力吃緊的狀況下，有一群台灣皮膚科醫師選擇貢獻一己之力，提供海內外民眾免費線上諮詢，用實際行動告訴全世界：「Taiwan can help！」…more

FREE RESOURCE

The Disease Map
疾病地圖

The introduced disease maps visualise how human disorders are 'positioned' against each other in a population with respect to an individual biological organism. It is well known that many human disorders are risk factors for or consequences of other disorders. While many of such positive associations between disorders are well known and understood, many remain either entirely unnoticed or not properly recognised. Moreover, it is reasonable to assume that certain human disorders can be associated negatively, that is, a person being affected with A, has decreased chances to get affected with B (can be, but not necessary, vice versa). The presented visualisation is a novel tool for validating already known as well as discovering new, never noticed, empirical association patterns between human disorders. While some of such patters can reflect specifics of a healthcare system, many can reveal interesting biological phenomena relevant practically.
1.S Syed-Abdul, M Moldovan, PA Nguyen, R Enikeev, WS Jian, U Iqbal, MH Hsu, YC Li, Profiling phenome-wide associations: a population-based observational study, 2015
2.M Moldovan, R Enikeev, S Syed-Abdul, PA Nguyen, YC Chang, YC Li, Disease universe: Visualisation of population-wide disease-wide associations, 2014

Phenome-wide Associations Database
疾病關聯資料庫

​To objectively characterize phenome-wide associations observed in the entire Taiwanese population and represent them in a meaningful, interpretable way.In this population-based observational study, we analyzed 782 million outpatient visits and 15,394 unique phenotypes that were observed in the entire Taiwanese population of over 22 million individuals. Our data was obtained from Taiwan’s National Health Insurance Research Database.
S Syed-Abdul, M Moldovan, PA Nguyen, R Enikeev,WS Jian, U Iqbal, MH Hsu, YC Li, Profiling phenome-wide associations: a population-based observational study, Journal of the American Medical Informatics Association, Volume 22, Issue 4, 1 

PROJECT

AI-enhanced Safety of Prescription and clinical application

Ministry of Science and Technology, 2019.07~2020.06
A recent study reported more than 250,000 people in the U.S. to die every year from medical errors. Medical errors are the third-leading cause of death after heart disease and cancer. Unsafe medication practices and medication errors are a leading cause of injury and avoidable harm in health care systems across the world. According to the WHO report, the cost associated with medication errors has been estimated at $42 billion USD annually. WHO launches global effort to salve medication-related errors in 5 years.

In order to decrease medication errors or near miss, a lot of hospitals used “Rule-based” to create a clinical decision support system. However, there are some limitations to practice. For example, there is about 50-90% overridden rate for alerts, not cost-effective, without Randomized Controlled Trial (RCT) and it could not learn to learning.
Our research team has been investing in AI - enhanced Safety of Prescription for a long time. Based on more than 1.3 billion past electronic medical records, we develop software which included unsupervised learning and reinforcement learning. AI-enhanced Safety of Prescriptions (AESOP) is able to simulate real-world physicians’ behaviors by the treatment patterns. It could apply to detect appropriateness of prescription. Now, there are more than 200 physicians are using our system in 250 K patients/year. Physicians accept rate is more than 50% and AESOP agreed rate reaches 85% based on expert reviews

Based on this project, we are expected to upgrade our system (Protection＆ prevention, Proactive Recommendations), develop reinforcement learning model, create hospital’s report、start clinical trial in US medical institutions, and promote startup business. Then, it can build brand products, increase entry barriers to, expand the market, and build the first AI medical unicorn in Taiwan.

Artificial Intelligence for Earlier and Safer Medicine Worldwide

Ministry of Science and Technology, 2018~2019
Aging population in Taiwan is growing rapidly. 12.5 percent of the population is aged 65 or older. Due to the incidence of chronic diseases and degenerative illnesses in the aging population, it has increased the chances of polypharmacy. On average, Taiwanese visits hospital about 14 times in a year and gets at least 4-5 medicines every time. Therefore, the total number of prescription per year is about 345 million. Research has shown that the rate of medication error is 0.5% to 5% which means approximately 0.75 - 7.5 million of dispensed prescriptions in Taiwan are inappropriate. The current clinical decision support system can not prevent medication error efficiently. It is also claimed that 193 tons of medicine have been wasted annually because of poor patient compliance in Taiwan.

Artificial Intelligence (AI) has been used extensively and is expected that machine learning, deep learning and natural language processing will become the major technology in next 5 to 10 years. AI, machine learning especially, is showing its potential in medicine field due to the availability of big data. As Taiwan has the world-class medical electronic data such as electronic medical records and health insurance database, it is a great opportunity to apply big data to develop AI in medicine.

In this project, we aim to develop artificial intelligence for earlier and safer medicine in Taiwan as well as worldwide which includes (1) Create model and system for earlier and safer medicine; (2) Develop artificial empathy; (3) Randomized controlled trial and (4) Medical institutions used and reinforcement learning. It will exhibit Taiwan's strengths to promote the wisdom of medical safety and enhance patient compliance. We will connect industry, government, academia, research and medical between Taiwan and other countries to build a AI with Humanity, for Humanity.
​
In the same time, this project will provide medical databases containing disease-disease, disease-medication and medication-medication association. By sharing medical resource, It will help to improve the application of medical treatments, ensure the safety and quality for healthcare.

Building MedGuard for Drug Safety

Ministry of Science and Technology, 2017.05~2017.12
Medication error is the most common medical error. Due to the complexity of drug treatment process, medication error could happen anytime when writing the prescription, manufacturing the formulation, dispensing the formulation, taking the medicine, and so on. The rule-based medication safety system is widely used to identify the accuracy of the medication information. However, there is about 50-90% overridden rate for alerts, it expressed that rule-based medication safety system is not useful. More, it is difficult to determine its feasibility and usefulness without Randomized Controlled Trial (RCT). With heightened concern about medication-use safety and medication errors, information technology (IT) could be a means to effectively lower the risk and the chance of medication errors.
​
In this project, we develop an AI model for advanced electronic safety of prescriptions. The prototype is based on Probabilistic method. We built up a knowledge base with “Big data” analyzing the associations between Disease-Medication (DM) and Medication-Medication (MM) and we further developed AOP model (Appropriateness of Prescription). The core technique has been applied in western prescriptions and Chinese prescriptions and also published in International Journals (PLOS One、pharmacoepidemiology and drug safety). We also get a U.S. patent (Patent US20150356272). According to Technology Readiness Level(TRL), MedGuard achieves TRL 6 (System/subsystem model or prototype demonstration in a relevant environment). 

This project will create project team and expert consultants team to help MedGuard achieve TRL 7-8 (product evaluation. MedGuard is a software module and it can be customized as APP, server, and Cloud. The main characters in the industry value chain are Health Administration or hospitals and pharmaceutical companies and insurance companies. Many statistics indicate that medication-use safety has great potential in business and value in terms of the patient safety, healthcare expenditure, medical audit fee, etc. Commercializing MedGuard not only reduce the risk of medication errors, but also improve healthcare quality and significantly decrease the medical waste and expense.

Enhancing Intelligent Healthcare with Usage of International Cooperative Research in Health Informatics

Ministry of Health and Welfare 2017.08~2018.04
The gradual aging of the Taiwanese population has strained the government’s ability to provide healthcare for such a large number of elderly citizens. This challenge has caused various healthcare policies to be enacted urgently. People have become more aware of the advances in medical technology that can improve their lives and now have higher expectations for how their own personal healthcare data should be leveraged to meet these expectations. Personalized healthcare, information technology, and cloud computing have opened up all kinds of new healthcare applications and possibilities. Information and communication technology as well as ehealth may lower costs and unite once disparate industries. Electronic health records are expected also to empower patients, increase healthcare literacy, increase safety, and encourage patient compliance. Patients can also participate in their own health diagnoses and make informed decisions.

Establishing an ehealth hub where various parties can share data is the main topic of this research proposal. This ehealth hub follows the Institute of Medicine’s 2007 promotion of Learning Health Systems. Charles P Friedman, a pioneer and professor with extensive experience in designing Learning Health Systems, was invited to be a mentor in this project. Through this international cooperation, sharing ideas, holding workshops, meetings, and incorporating the perspectives from different domains can be possible within this ehealth hub.

​Smoking is the largest avoidable cause of preventable morbidity and premature mortality worldwide. The prevalence of smoking worldwide is estimated at about one billion smokers, half of which will die prematurely as a consequence of their addiction. Smoking causes approximately 85% of the 3 cases of lung cancer and chronic obstructive pulmonary disease (COPD) and contributes to the development of many other lung diseases, such as Cancer of the trachea, bronchus and lung. We've passed a European Union’s Horizon 2020 research about smoke free brain. Using the wearable technology plus a mobile App that is programmed to push tailored messages for health concern and readiness to quit, tips or sustaining abstinence, use of interactive self-assessments, helpful cessation information, and access to physical activity charts.

​These database will be collected to become knowledge, through LHS system to mechanical learning. With the advancement in the medical sciences, cancers are now considered as chronic disease rather than fatal illness. This moves the focus in the fight against cancer from sustaining life towards maximizing functional capacity and Quality Of Life (QOL). A critical element in this shift has been the rise of active rehabilitation in the management of cancer. We will cooperate with CATCH project which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 722012.

Furthermore, overall physical activity and health behavioural change is increasingly becoming important in cancer management. Technology advances such as gamification based on biofeedback, and neuromuscular electrical stimulation, can help address some of these information and communication technology into LHS cycle. To improve cancer patients physical activity, health behavioural and QOL. Keywords: Information and Communication Technology (ICT), eHealth, Learning Health System, Electronic Health Record, wearable technology, International Collaboration. 
Yu-Chuan (Jack) Li
College of Medical Science and Technology
​Taipei Medical University
       

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