Large Language Models for Biomedical Applications
Abstract:
The landscape of natural language processing (NLP) has been significantly transformed by recent advancements in Large Language Models (LLMs). In the biomedical domain, LLMs-based approaches and solutions have demonstrated its potential to revolutionize biomedical research and clinical practice. This presentation will concentrate on our recent endeavors in developing methodologies and software tailored for important biomedical applications, based on state-of-the-art LLMs. We will explore the utilization of both open-source and closed-source LLMs, including LLaMA2/3 and GPT-4, on diverse tasks such as information extraction, questions answering, and literature mining. Additionally, we will delve into the valuable insights gleaned from employing LLM-based approaches in biomedical applications.
Bio
Dr. Hua Xu is Robert T. McCluskey Professor and Vice Chair for Research and Development, Department of Biomedical Informatics and Data Science at Yale School of Medicine (YSM), as well as Assistant Dean for Biomedical Informatics at YSM. He received his Ph.D. in Biomedical Informatics from Columbia University. His primary research interests include biomedical natural language processing (NLP) and data mining, as well as their applications in secondary use of electronic health records data for clinical and translational research. His research is funded by multiple agencies (i.e., NLM, NCI, NIGMS, NIA, AHA, and CPRIT), and methods/tools developed in his lab have been widely used to support diverse biomedical applications. He served as the Chair of American Medical Informatics Association (AMIA) NLP Working Group and now leads the Observational Health Data Sciences and Informatics (OHDSI) NLP Working Group. Dr. Xu is a fellow of both the American College of Medical Informatics (ACMI) and the International Academy of Health Sciences Informatics (IAHSI).
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