Symposium 2: AI/ML Applications in Pharmacoepidemiology: Risk Identification and Management in Asian Populations
Tracks
Track 2
| Saturday, November 22, 2025 |
| 10:45 - 12:15 |
Details
The rapidly evolving healthcare landscape in Asia faces unique challenges in pharmacovigilance and risk management, including diverse genetic populations, varying healthcare infrastructures, and the need for scalable solutions across different healthcare systems. Traditional pharmacoepidemiological methods often fall short in processing the vast amounts of real-world data generated across Asian healthcare networks, leading to delayed identification of at-risk patients and suboptimal therapeutic outcomes. Critical gaps exist in early disease detection, timely identification of adverse drug reactions, and proactive risk stratification for hospitalization and mortality. The integration of artificial intelligence and machine learning technologies presents unprecedented opportunities to transform these challenges into actionable insights, yet implementation barriers and methodological considerations specific to Asian populations remain underexplored.
This symposium will present cutting-edge applications of AI/ML methodologies in three key domains of pharmacoepidemiological practice:
Session 1: Early Diagnosis and Disease Detection
• Machine learning algorithms for predictive modeling using electronic health records
• Natural language processing for extracting clinical insights from unstructured data
Session 2: Intelligent Drug Safety Surveillance
• AI-powered adverse event identification and causality assessment
Session 3: Risk Stratification and Outcome Prediction
• Deep learning models for hospitalization and mortality risk prediction
• Population-specific risk algorithms accounting for Asian demographic and clinical characteristics
Each session will combine theoretical foundations with practical applications, featuring interactive demonstrations and collaborative discussions on implementation challenges and solutions.
Upon completion of this symposium, participants will be able to:
1. Evaluate the current state and potential of AI/ML applications in pharmacoepidemiology, specifically within Asian healthcare contexts
2. Apply machine learning methodologies for early disease detection and risk identification using real-world evidence
3. Implement AI-powered drug safety surveillance systems for enhanced pharmacovigilance practices
4. Develop risk stratification models for predicting patient outcomes including hospitalization and mortality
5. Design implementation strategies that account for regional variations in healthcare infrastructure and patient populations
Speaker
Rui She
Research Assistant Professor
Faculty of Health and Social Sciences, The Hong Kong Polytechnic University
Symposium Moderator
Biography
Dr. Rui She (Sherry) received her double bachelor’s degrees in Medicine and Economics from Peking University, and her PhD and postdoctoral training at The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong. She is currently working as the research assistant professor at Hong Kong Polytechnic University. She had been an academic visitor at several prestigious institutions and established extensive collaborations with them, including Karolinska Institutet, Nanyang Technological University, Uppsala University, and Duke Kunshan University.
Dr. She’s research interests include using population studies to understand the patterns and impacts of multimorbidity and the aging process, as well as employing behavioral interventions to improve chronic disease management for better patient outcomes and in different healthcare settings. Dr She had successfully secured several external grants as the Principal Investigator to enhance the self-management, prognosis, and rehabilitation of patients with cardiovascular diseases (e.g., hypertension, stroke).
Dr. Aimin Yang
Research Assistant Professor
Faculty of Medicine, the Chinese University of Hong Kong
Symposium Moderator and Presenter
Biography
Dr. Aimin Yang is a Research Assistant Professor in the Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong. Dr. Yang’s research interests focus on the epidemiology and pharmacoepidemiology of diabetes and cardiometabolic diseases, as well as the application of advanced statistical and machine-learning algorithms to analyze large-scale electronic health record (EHR) data. He leads and contributes to multiple health research projects and has authored or co-authored over 100 peer-reviewed scientific articles. He also serves on the editorial board for the journal Diabetes, Obesity and Metabolism.
Prof Wai Kit Ming
Associate professor
Department of Infectious Diseases and Public Health, City University of Hong Kong
Symposium Presenter
Biography
Professor MING Wai Kit is an Associate Professor in Public Health and Epidemiology at City University of Hong Kong. He also serves as Assistant Director of the Institute of Global Governance and Innovation and Programme Leader of the Master of Public Health (MPH). A seasoned clinician and educator, he brings over 15 years of experience in teaching, mentoring, and supervising more than 100 undergraduate and postgraduate research students. Prof. Ming completed postdoctoral fellowships at the University of Oxford and Harvard Medical School.
His interdisciplinary research spans maternal and child health, infectious disease epidemiology, artificial intelligence in medicine, health policy, and health economics. He has authored over 150 peer-reviewed publications, accumulating more than 5,000 citations and an h-index of 31. Recognized among the world’s top 2% most-cited scientists, he actively contributes to public health policy and innovation through numerous advisory and expert roles in both Hong Kong and Mainland China.
Assistant Professor Shanquan Chen
Assistant professor
London School of Hygiene & Tropical Medicine
Symposium Presenter
Biography
Dr. Shanquan Chen is an Assistant Professor at the University of Hong Kong specializing in epidemiology, mental health, and health economics. His research addresses critical public health challenges in dementia, aging, and functional disability through longitudinal studies, randomized controlled trials, and advanced analytics. As first author, he has led influential publications on temporal trends in dementia risk factors (The Lancet Regional Health), the impact of living costs on mental health service utilization (Nature Mental Health), and the application of machine learning in life-course epidemiology (BMC Medicine). Beyond academia, he has served as consultant to the World Bank and UNICEF, driving evidence-based policy and improving healthcare access for vulnerable populations. He also plays a leading role in the European China Health Economics and Outcomes Research Association, fostering collaboration between academic and industry partners. Passionate about impact, he translates research into practice through teaching, mentorship, and public engagement.
Dr. Sha Feng
Associate professor
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
Symposium Presenter
Biography
Sha Feng is an associate researcher at the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. He received his bachelor’s degree in Statistics from Sun Yat-sen University, a master’s degree in Statistics from the University of Pennsylvania, and a Ph.D. in Population Health from the University of Hong Kong. His research focuses on computational epidemiology, integrating artificial intelligence methods with epidemiological approaches to advance clinical research on dementia, including screening, interventions, and the identification of novel risk factors. He was recognized among the world’s top 2% of scientists by ESI in 2022 and 2023 and has accumulated more than 25,000 citations on Google Scholar. He also serves as an editorial board member for BMC Medicine (Neurology section) and is a reviewer for The BMJ.
Da Feng
Huazhong University of Science and Technology
Symposium Presenter
Biography
Da Feng is the associate professor in pharmacy college of Huazhong University of Science and Technology in China, her research is focused on pharmaceutical care, polypharmacy management, Health Technology Assessment (HTA) and pharmacoeconomics. She has published nearly 30 academic papers in recent 5 years and held 3 projects National Natural Science Foundation of China , including Bill Gates and Melinda Foundation project. She focuses on mainly include elderly patients with chronic diseases, and vaccines reasonable use in real world and tries to develop machine learning mothod in clinical drug use in the future research.
Dr Fan Jiang
Shandong University
Symposium Presenter
Biography
Dr. Jiang Fan is a Research Fellow in the Department of Epidemiology & Biostatistics, Shandong University. Her research focuses on the epidemiology of sensory aging and its association with major non-communicable diseases. A key area of her work involves leveraging technology for public health. In partnership with Tencent Aging Lab, she led the development and validation of 'AudioMate,' a smartphone-based screening system for scalable risk identification. She co-leads a large-scale, multi-center cohort study with over 5,000 participants, integrating complex data from sensory assessments, biological markers, and functional measures to inform risk management strategies in Asia population. Her expertise includes data science, advanced statistical modeling, and designing digital health interventions to translate research into scalable community health solutions.
Yuanxuan Cai
Huazhong University of Science and Technology
Symposium Presenter
Biography
Yuanxuan Cai is a PhD candidate at the School of Pharmacy, Huazhong University of Science and Technology, China. Her research focuses on drug safety and pharmacoepidemiology, with particular emphasis on adverse drug reactions and risk prediction in real-world Asian populations. She has participated in studies examining safety signals of cephalosporins in children and adverse events associated with statin use in Chinese cohorts. In addition, she has been involved in research on oxaliplatin-related hypersensitivity reactions and liver injury, where statistical methods and machine learning approaches were applied to improve risk prediction. Her future work will focus on the safety of drug combination and the development of predictive models based on real-world data, with the goal of advancing early risk identification and supporting safer and more rational medication use in Asian contexts.
