​DHA 2024

Conference Keynote Speakers

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                                       Professor Andrew Burton-Jones

                                       The University of Queensland, Australia

                                       Former MISQ Editor in Chief

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Topic: To be confirmed

Abstract: To be confirmed

Biography: Andrew Burton-Jones is a Fellow of the Association for Information Systems, Fellow of the Academy of Social Sciences of Australia, and Former Editor-in-Chief of MIS Quarterly (2021-2023). He undertakes research in the areas of how effectively organisations use IT, improving methods to analyse and design IT systems, improving theories and methods used by researchers in the Information Systems discipline. He has published in and served on the editorial boards of many journals, including the Journal of the Association for Information Systems, Information Systems Research, MIS Quarterly, Information and Organization, and Academy of Management Discoveries

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                                       Professor Zhongsheng Hua

                                       Distinguished Young Scholars (NSFC)
                                       School of Management, Zhejiang University

                                       

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Topic: An Interpretable Machine Learning Framework for Predictive Modelling: Taking Peri-implantitis as an Example

Abstract:With the advancement of research, there is a growing tension between performance and interpretability in machine learning (ML), especially in high-stakes domains such as healthcare. As a promising solution, we propose a generic framework that integrates ML-based predictive modeling with game-theory-inspired interpretative tools. For implementation, peri-implantitis as a scenario of healthcare is selected. We first construct, calibrate, and evaluate multiple ML models which aims to predict the onset of peri-implantitis and estimate the associated risk probability. The calibrated random forest model exhibits the best performance with multiple metrics in the 90%+ range, surpassing existing state-of-the-art works. Subsequently, we provide model interpretations on the basis of the Shapley value and the levels structure value (LSV). The evaluation demonstrates that the LSV, which accounts for the correlation between the feature variables, presents superior interpretability. The global interpretability offers a holistic perspective on the model by identifying potential risk indicators, while the local interpretability quantifies the impact of indicators on the risk probability for a specific implant. Such insights derived from the pipeline are anticipated to enhance the decision-making capabilities of healthcare professionals and clinicians, possibly contributing to improved patient outcomes.

Biography: Zhongsheng Hua is a Professor at the School of Management of Zhejiang University. Prof. Hua is engaged in the research of service services and operations management. His research has been supported by the National Science Fund for Distinguished Young Scholars of National Natural Science Foundation of China (NSFC for short), and Major Program of NSFC. His work has appeared in Production and Operations Management, Journal of Operations Management, Marketing Science, IEEE Trans. He has published more than 200 papers in peer-reviewed journals. He was selected by Elsevier's list of Most Cited Chinese Researchers from 2014 to 2022.

 

 

 

                                       Professor Xitong Guo

                                       Distinguished Young Scholars (NSFC)
                                       Executive Director of the Institute of Electronic Health, and

                                       Professor in Harbin Institute of Technology, China

                                       

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​Topic: Empowering Patients Using Smart Mobile Health Platforms: Evidence from A Randomized Field Experiment

Abstract:With today’s technological advancements, mobile phones and wearable devices have become extensions of an increasingly diffused and smart digital infrastructure. In this paper, we examine mobile health (mHealth) platforms and their health and economic impacts on the outcomes of chronic disease patients. To do so, we partnered with a major mHealth firm that provides one of the largest mobile health app platforms in Asia specializing in diabetes care. We designed and implemented a randomized field experiment based on detailed patient health activities (e.g., steps, exercises, sleep, food intake) and blood glucose values from 1,070 diabetes patients over several months. Our main findings show that the adoption of the mHealth app leads to an improvement in health behavior, which in turn leads to both short term metrics and longer-term metrics. Patients who adopted the mHealth app undertook higher levels of exercise, consumed healthier food with lower calories, walked more steps and slept for longer times on a daily basis. They also were more likely to substitute offline visits with telehealth. A comparison of mobile versus PC-enabled versions of the same app demonstrates that the mobile version has a stronger effect than PC version in helping patients make these behavioral modifications with respect to diet, exercise, and lifestyle, which ultimately leads to an improvement in their healthcare outcomes. We also compared outcomes when the platform facilitates personalized health reminders to patients vis-à-vis generic (non-personalized) reminders. Surprisingly, we found that personalized mobile messages with patient-specific guidance can have an inadvertent (smaller) effect on patient app engagement and lifestyle changes, leading to a lower health improvement. However, they are more like to encourage a substitution of offline visits by telehealth. Overall, our findings indicate the massive potential of mHealth technologies and platform design in achieving better healthcare outcomes.

Biography: Xitong Guo is Professor of Information Systems at the school of Management in Harbin Institute of Technology as well as the director of eHealth research institute. He received his Ph.D. in Information Systems at the City University of Hong Kong and Ph.D. in Management Science and Engineering at the University of Science and Technology of China. His research focuses on e-Health. His work has been published in referred journals, including MIS Quarterly, Information Systems Research, Production and Operations Management, Journal of Operations Management, Journal of Management Information Systems, among others.

 

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                                       Professor Luxia Zhang

                                       Distinguished Young Scholars (NSFC)
                                       Deputy Dean of the National Institute of Health Data

                                       Science at Peking University, and Professor in the Renal 

                                       Division of Peking University First Hospital, China

                                       

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​Topic: Health Data Science: data for better health

Abstract:The rapid advancement of big data and artificial intelligence (AI), including generative AI (GAI), has ushered in a new era in the medical field, promising transformative changes in healthcare delivery and outcomes, which has consequently given rise to a new discipline - Health Data Science. This lecture will provide a brief introduction to the emerging discipline of health data science. Then I will highlight its applications in areas such as disease surveillance, disease risk prediction, data value enhancement, and addressing unmet needs, using research examples. Additionally, the ethical considerations and data privacy challenges associated with the use of big data and AI in healthcare will be discussed.

Biography:Dr. Luxia Zhang is the Deputy Dean of the National Institute of Health Data Science at Peking University, China, and Professor in the Renal Division of Peking University First Hospital, China. She obtained her MD degree at Peking University and her MPH degree at Harvard School of Public Health. Her research focuses on prevalence, risk factors, intervention, and management of kidney disease in China. Most of her work provides first-hand information on kidney disease in China and has gained wide attention internationally. During the last several years, her study interests have been expanded to the management of major non-communicable chronic diseases by leveraging the power of big data and artificial intelligence. Her studies have been published in top medical journals including N Engl J Med, the Lancet and Br Med J. Dr. Zhang was named on the list of the "World's Top 2% Scientists 2020-2022" from Stanford University and the "China Highly Cited Scholars" list in 2020-2022 from Elsevier. She is the Vice President of Health Data Application and Management Committee, Chinese Hospital Association; Deputy Editor of Health Data Science (a Science Partner Journal); member of the Lancet Digital Health International Advisory Board; and member of Editorial Boards of Clin J Am Soc Nephrol and Am J Kidney Dis.

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                                       Professor Zhidong Cao

                                       Distinguished Young Scholars (NSFC)
                                       Research Fellow in the State Key Laboratory of

                                       Management and Control for Complex Systems,

                                       Institute of Automation, Chinese of Academy

                                       

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​Topic: Advancements in Large Model Technology and Their Influence on Healthcare

Abstract:Propelled by big data, high computational power, and deep learning algorithms, the new generation of artificial intelligence is progressively advancing from perceptual intelligence to cognitive intelligence. Large model technology, distinguished by its versatility, fluency, smoothness, and natural human-computer interaction, is emerging as a groundbreaking technological phenomenon. This presentation will share the research team's insights, explorations, and practices in the realm of large model technology. It will provide a comprehensive overview of the development of large models in healthcare both domestically and internationally, and discuss the evolutionary trajectory of how healthcare development, initially driven by big data, is now transitioning to being driven by large models from a technology-driven perspective.

Biography: Dr. Zhidong Cao, Ph.D. in Science, is currently a researcher and Ph.D. advisor at the State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences. He is a recipient of the National Science Fund for Distinguished Young Scholars and serves as the principal investigator (chief scientist) for the Major Project of Science and Technology Innovation 2030—"New Generation of Artificial Intelligence." Dr. Cao has been recognized as an advanced individual in the national science and technology system for his efforts in combating the COVID-19 pandemic. He serves as a committee member, council member, and executive council member in several national first-level societies. Dr. Cao has participated in major national science and technology projects, key national R&D programs, major research plans of the National Natural Science Foundation, and the National Mid- to Long-Term Science and Technology Development Plan (2021-2035), as well as the New Generation Artificial Intelligence Strategic Plan. He has led and undertaken more than 20 national-level projects, topics, and tasks, including six projects funded by the National Natural Science Foundation. Dr. Cao has published over 140 research papers in authoritative journals and conferences both domestically and internationally, authored three books, and received ten provincial and ministerial-level science and technology awards.

 

 

 

                                       Professor Yuming Zhang

                                       Director of the Medical Health Big Data and Network

                                       Research Center (East China), China Academy of

                                       Information and Communications Technology (CAICT), and

                                       General Manager of Digital Health Division, Industrial   

                                       Internet Innovation Center (Shanghai) Co., Ltd.

                                       

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​Topic: The Digital Intelligence Era: Stepping into the Metaverse Medical Ecosystem

Abstract: In the Digital Intelligence Era, the integration of metaverse technology into healthcare is revolutionizing the industry, forming a new metaverse medical ecosystem. This presentation will explore the transformative potential of the metaverse in enhancing medical education, virtual diagnostics, remote healthcare, and personalized medicine. Key areas include the use of virtual reality for medical training, the role of wearable devices in real-time health monitoring, and the application of big data for public health surveillance. Additionally, we will discuss the ethical and legal challenges associated with this technology. By fostering multidisciplinary collaboration, we aim to demonstrate how the metaverse can advance healthcare innovation and improve patient outcomes.

Biography: Mr. Yuming Zhang currently works at the China Academy of Information and Communications Technology, serving as the director of the Healthcare Big Data and Network Research (East China) Center, and concurrently serving as the general manager of the Digital Health Division of the Industrial Internet Innovation Center (Shanghai) Co., Ltd. In his more than 20 years of work experience, he has focused on the medical device industry and has rich experience in marketing, corporate management, strategic planning, industry information, etc. He mainly studies the integration of new generation communication technology and the medical and health industry, and is committed to the application and promotion of new technologies in the medical field.​

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