This BMI seminar series is designed to introduce state-of-the-art research in Computational Biology/Bioinformatics, Biostatistics, Clinical Informatics, and Artificial Intelligence to the scientists within BMI as well as to engage researchers from a variety of academic disciplines throughout the University. The seminar series serves as a platform for sharing knowledge, fostering collaboration, and promoting intellectual discourse within our department and beyond. We believe that the diversity of the speakers’ expertise and interests can contribute greatly to the success of the seminar series.
Speaker: Jimeng Sun
Abstract:
Clinical trials are essential for advancing medical knowledge and developing new treatments, but they often face significant challenges in efficiency and cost-effectiveness. This talk explores the transformative potential of AI in revolutionizing the clinical trial process. We will examine seven key AI applications that can dramatically improve trial speed, accuracy, and outcomes: protocol design optimization, automated systematic review of existing literature, patient-trial matching, enhanced trial data analysis, predictive modeling of trial outcomes, feasibility analysis, and data-driven site selection. This talk will highlight the technical challenges and opportunities in each area, providing a roadmap for computer scientists to make a significant impact in improving clinical trials and drug development processes.
Speaker Biography:
Dr. Sun is a Health Innovation Professor at the Computer Science Department and Carle Illinois College of Medicine at University of Illinois Urbana Champaign. Previously, he was an associate professor at Georgia Tech's College of Computing and co-directed the Center for Health Analytics and Informatics. Dr. Sun's research focuses on using artificial intelligence (AI) to improve healthcare. This includes deep learning for drug discovery, clinical trial optimization, computational phenotyping, clinical predictive modeling, treatment recommendation, and health monitoring. He has been recognized as one of the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare. He collaborates with leading hospitals such as MGH, Beth Israel Deaconess, OSF healthcare, Northwestern, Sutter Health, Vanderbilt, Geisinger, and Emory, as well as the biomedical industry, including IQVIA, Medidata and multiple pharmaceutical companies. Dr. Sun earned his B.S. and M.Phil. in computer science at Hong Kong University of Science and Technology, and his Ph.D. in computer science at Carnegie Mellon University.
Abstract:
Viewing laboratory test results is the most frequent activity for patients accessing patient portals, yet the interpretation of these results can be challenging and confusing. While previous research has explored various methods of presenting lab results, there has been limited focus on providing tailored information support based on an individual's medical context. Our AHRQ-funded LabGenie project addresses this gap by developing a user-centered, web-based tool designed to enhance patient engagement and understanding of lab results, particularly for older adults with multiple chronic conditions who often face difficulties due to limited health literacy and technology skills.
In this talk, I will present a series of studies that investigate the challenges older adults face in understanding lab test results and our efforts to leverage generative AI and advanced biostatistical methods to provide better support. Our formative research included a survey of 270 patients to identify factors affecting their comprehension of lab results. We have conducted a series of design workshops to evaluate visualizations of lab results. Additionally, we
evaluated the effectiveness of four large language models (LLMs) for answering lab-related questions sourced from a community Q&A website. To further enhance the accuracy of lab test interpretation, we analyzed the seasonality of lab results in a cohort of older adults with Alzheimer’s disease. We also used knowledge-augmented LLMs to determine normal ranges of various condition, predict differential diagnoses using clinical vignettes from case reports, and generate tailored questions for patients to follow with their doctors.
Through these studies, we aim to develop innovative, AI-driven approaches that improve the accessibility and comprehension of lab results for older adults, ultimately promoting better patient engagement and shared decision-making in healthcare
Speaker Biography:
Dr. Zhe He is Interim Director for Florida State University Institute for Successful Longevity and an Associate Professor in the School of Information (iSchool). He is also Director of Biostatistics, Informatics, and Research Design (BIRD) Program in the UF-FSU Clinical and Translational Science Award Hub. His research expertise includes machine learning, natural language processing, and knowledge representation. The overarching goal of his research is to improve population health and advance biomedical research through the application of informatics and data science. His research has been funded by National Institute on Aging, National Library of Medicine, National Institute of Mental Health, and Agency for Healthcare Research and Quality.
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