Pre-Trained Large Language Models’ Utility for Food Concept Normalization

Presentation Time: 11:15 AM - 11:30 AM

Abstract Keywords: Patient / Person Generated Health Data (Patient Reported Outcomes), Large Language Models (LLMs), Precision Medicine, Information Extraction, Natural Language Processing, Controlled Terminologies, Ontologies, and Vocabularies
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

Patient-generated health data facilitates more informed precision nutrition interventions, but patient-generated health data is heterogeneous. This limitation can be mitigated by applying concept mapping techniques to standardize patient-generated health data. However, current approaches struggle with processing abbreviations and food brand detection. In this study, we argue that pre-trained large language models can improve concept mapping when applied to patient-generated free text meal records, which addresses the above challenge presented by patient-generated health data.

Speaker(s):
Adit Anand, B.S.
Columbia University

Author(s):
Yanwei Li, BS - Columbia University; Lena Mamykina, PhD - Columbia University; Chunhua Weng, PhD - Columbia University;