Filling the gaps: leveraging large language models for temporal harmonization of clinical text across multiple medical visits for clinical prediction

Poster Number: P173
Presentation Time: 05:00 PM - 06:30 PM

Abstract Keywords: Large Language Models (LLMs), Deep Learning, Natural Language Processing, Machine Learning, Health Equity, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

Electronic health records offer great promise for early disease detection, treatment evaluation, information discovery, and other important facets of precision health. Clinical notes, in particular, may contain nuanced information about a patient’s condition, treatment plans, and history that structured data may not capture. As a result, and with advancements in natural language processing, clinical notes have been increasingly used in supervised prediction models. To predict long-term outcomes such as chronic disease and mortality, it is often advantageous to leverage data occurring at multiple time points in a patient’s history. However, these data are often collected at irregular time intervals and varying frequencies, thus posing an analytical challenge. Here, we propose the use of large language models (LLMs) for robust temporal harmonization of clinical notes across multiple visits. We compare multiple state-of-the-art LLMs in their ability to generate useful information during time gaps, and evaluate performance in supervised deep learning models.

Speaker(s):
Inyoung Choi, B.S.
University of Pennsylvania