Yu-Chuan Jack Li is a respected expert in artificial intelligence in medicine and translational biomedical informatics. His exceptional accomplishments have earned him a prominent position among the top 2% of scientists worldwide.
His active involvement in international collaborations across Asia, the United States, Europe, and Africa demonstrates his significant global impact. As the principal investigator of numerous national projects on translational biomedical informatics, patient safety, and medical AI, Li has led efforts to advance the use of AI in disease prevention, focusing on "earlier medicine," safety, and quality. His research on temporal phenomic stochastic models has developed the largest model ever constructed, incorporating medical data elements like medications, diagnoses, lab tests, examinations, and procedures.
Throughout his career, Li has dedicated himself to bridging the gap between clinical research, practice, and business. His exceptional achievements have earned him fellowships at prestigious institutions such as the Australian College of Health Informatics, the American College of Medical Informatics, and the International Academy of Health Science Informatics. He has also received numerous awards for his remarkable contributions.
Li previously served as President of the International Medical Informatics Association (IMIA), an NGO affiliated with the World Health Organization (WHO), dedicated to connecting professionals worldwide to advance biomedical and health informatics science, education, and practice.
Li is currently a Distinguished Professor at Taipei Medical University, where he imparts his expertise to future medical professionals. He is also a practicing dermatologist at Taipei Municipal Wanfang Hospital and serves as the President of the Taipei Medical University Doctors’ Association, remaining actively engaged in clinical practice and addressing real-world healthcare challenges. Additionally, he is the Editor-in-Chief of the BMJ Health & Care Informatics journal, contributing to the dissemination of knowledge in the field.
⎯ Research Tag ⎯
His active involvement in international collaborations across Asia, the United States, Europe, and Africa demonstrates his significant global impact. As the principal investigator of numerous national projects on translational biomedical informatics, patient safety, and medical AI, Li has led efforts to advance the use of AI in disease prevention, focusing on "earlier medicine," safety, and quality. His research on temporal phenomic stochastic models has developed the largest model ever constructed, incorporating medical data elements like medications, diagnoses, lab tests, examinations, and procedures.
Throughout his career, Li has dedicated himself to bridging the gap between clinical research, practice, and business. His exceptional achievements have earned him fellowships at prestigious institutions such as the Australian College of Health Informatics, the American College of Medical Informatics, and the International Academy of Health Science Informatics. He has also received numerous awards for his remarkable contributions.
Li previously served as President of the International Medical Informatics Association (IMIA), an NGO affiliated with the World Health Organization (WHO), dedicated to connecting professionals worldwide to advance biomedical and health informatics science, education, and practice.
Li is currently a Distinguished Professor at Taipei Medical University, where he imparts his expertise to future medical professionals. He is also a practicing dermatologist at Taipei Municipal Wanfang Hospital and serves as the President of the Taipei Medical University Doctors’ Association, remaining actively engaged in clinical practice and addressing real-world healthcare challenges. Additionally, he is the Editor-in-Chief of the BMJ Health & Care Informatics journal, contributing to the dissemination of knowledge in the field.
⎯ Research Tag ⎯
CURRENT POSITION
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Distinguished Professor
Graduate Institute of Biomedical Informatics
Taipei Medical University |
Dermatologist
Department of Dermatology
Taipei Medical University - Wanfang Hospital |
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Editor-in-Chief
BMJ Health & Care Informatics journal
(the official journal of FCI and published by BMJ) |
President
Taipei Medical University Doctor Association
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EDUCATION
Taipei Medical UniversityM.D., Medicine 1984-1991 |
University of Utah
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HONORS
Inaugural FellowInternational Academy of Health Sciences Informatics (IAHSI) International Medical Information Association 2017 |
Elected FellowAustralasian College of Health Informatics (ACHI) The Australasian College of Health Informatics 2010 |
Elected FellowAmerican College of Medical Informatics (ACMI) American Medical Informatics Association 2010 |
The 19th National Innovation AwardAcademic Research
Innovation Research Center for Biotechnology and Medicine Policy 2022 |
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2024 Smart City - 中保科技ESG論壇
中保科技
Mar 21 ,2024
Mar 21 ,2024
MEDTEX Summit Asia 2023生策會、生策中心 Nov 30 ,2023 |
MedInfo23
Australasian Institute of Digital Health, International Medical Informatics Association, NSW Ministry of Health July 8 -12 ,2023 |
中国医院协会县(市)医院分会
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5th MedTex Summit Asia 2022Institute for Biotechnology and Medicine Industry, International Medical Informatics Association Dec 04, 2022 |
AI and Its Failures in HealthcareDT4H Seminar Series The University of Melbourne Apr 21, 2022 |
Bridging the Gap between Academia and Commercialization: the Healthcare PerspectiveBioASIA Taiwan Jul 28, 2022 |
Leveraging Medical Resources in TaiwanHealthForAll Oct 22, 2020 |
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DATA MINING LARGE INTERNET NETWORKS TO PREDICT COVID-19 HOTSPOTSHarvard Global Health Institute, Novartis Foundation and partners Webinar | 2020.05.12 |
Dr. Yu-Chuan Li on post-pandemic era: the application of AI in precision diagnosis and treatmentGASE Taiwan | 2020.07.31 |
科創講堂
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真正的上醫要醫未病之病TEDxTaipei 2019 年會：Come．Unity 我，我們 TEDxTaipei Taiwan | 2019.10.06 |
探索手機力量！
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探索人類疾病的宇宙TEDxXinyi Taiwan | 2014.03.04 |
SELECTED PUBLICATIONs
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
“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
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
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
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
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
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
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醫學資訊管理學
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醫療資訊管理概論作者：龐婷文, 李友專等 華格那出版有限公司 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 華杏出版股份有限公司 2006 ISBN：9861-9400-06 |
國際醫院評鑑
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病人安全-理論與實務主編：邱文達, 李友專等 台灣醫務管理學會 2004 ISBN：9570-1725-09 |
醫療資訊管理學作者：吳昭新, 李友專等 偉華書局有限公司 2002 ISBN：9576-4050-68 |
簡明醫學資訊學總審閱：李友專 合記圖書出版社 2002 ISBN：9576-6688-75 |
PATENT
Clinical Association Evaluating Apparatus And Clinical Association Evaluating Method#I794863 |
Apparatus And Method For Processing Of A Prescription#I783172 |
Method For Providing Visualized Clinical Information And Electronic Device#I781674 |
Monitoring System, Device And Computer-Implemented Method For Monitoring Pressure Ulcers#I777132 |
Computer-Implemented Method And Computing Device For Predicting Cancer#I770591 |
Computing Device For Predicting Cancer#M614191 |
Method And System For Analyzing Skin Texture And Skin Lesion Using Artificial Intelligence Cloud Based Platform#I728369 |
Computing Device And Portable Device For Predicting Major Adverse Cardiovascular Events#M603615 |
System For Analyzing Skin Texture And Skin Lesion Using Artificial Intelligence Cloud Based Platform#M586599 |
Intelligent Personalize Medication Reminding System#I499995 |
Ultrasonic And Ozone Cleaning Device Without Water Sink#M444228 |
Mobile Personal Electronic Health Record System#M409487 |
Smart Cart for Medical Work#M356501 |
Prescription Analysis System and Method for Applying Probabilistic Model Based on Medical Big Data#US20150356272A1 |
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北醫成立「數位健康新創轉譯產學聯盟」 助數位科技落實臨床環球生技月刊｜Apr 29, 2024
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關鍵是健保資料開放
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運用A I減少醫療錯誤，就是現在自由時報｜Apr 12, 2024
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不抽血、不穿刺採樣，電腦「先知」 算出一年後的罹癌風險癌症問康健｜Jan 03, 2024
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每位癌症病人都獨一無二！有智慧醫療，才做得到精準治癌康健 ：癌症智慧醫療完全解析專刊｜Dec 22, 2023
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Seguridad del paciente en clave de inteligencia artificialDirectivos｜Nov 07, 2023
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國科會攜手國內外智慧醫院專家
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取得第20屆主辦權
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How Artificial Intelligence Is Transforming Future of HealthcareTaiwan Talks｜Feb 23, 2023
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巨變將來臨？
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ChatGPT趨勢難擋
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ChatGPT奇異點要來了嗎未來聊天室｜Mar 24, 2023
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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
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
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.
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.
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.
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.
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.