A generative foundation model for structured patient trajectory data

Presentation Time: 08:45 AM - 09:00 AM

Abstract Keywords: Bioinformatics, Clinical Decision Support, Deep Learning, Large Language Models (LLMs), Knowledge Representation and Information Modeling, Real-World Evidence Generation, Internal Medicine or Medical Subspecialty, Data Mining
Primary Track: Applications
Programmatic Theme: Clinical Informatics

Advancements in artificial intelligence propelled the implementation of general-purpose multitasking agents called foundation models. However, it has been challenging for foundation models to handle structured longitudinal medical data due to the mixed data types and variable timestamps in these data. Acquiring large training data is another obstacle. This study proposes a generative foundation model to manage patient trajectory data of variable lengths with mixed data types (categorical and continuous variables). Additionally, we propose a data pipeline to supply real-world data large enough to support foundation models. We locally obtained a large clinical dataset with a reproducible data pipeline scheme that leveraged a national HL7 message standard. Our trained model acquired the ability to suggest clinically relevant medical concepts and continuous variables for general purposes. The model also synthesized a database of more than 10,000 realistic patient trajectories. Our results suggest promising future downstream clinical applications of the foundation model.

Speaker(s):
Yu Akagi, M.D.
The University of Tokyo

Author(s):
Yu Akagi, M.D. - The University of Tokyo; Tomohisa Seki, MD PhD - the University of Tokyo hospital; Yoshimasa Kawazoe, M.D., Ph.D. - The University of Tokyo; Toru Takiguchi, M.D., Ph.D - The University of Tokyo; Kazuhiko Ohe, MD - University of Tokyo Hospital;