Research Symposium

24th annual Undergraduate Research Symposium, April 3, 2024

Angelique Deville Poster Session 2: 10:45 am - 11:45 am/402


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BIO


I am a Presidential Scholar in the Honors Program at FSU and originally from Prairieville, Louisiana. I am currently researching in the eHealth Lab on large language models in health informatics. I aim to continue exploring health informatics in the future to understand how artificial intelligence will influence medical care and diagnosis as more people start to rely on this technology. I am currently on the pre-medical track and involved in the following student organizations: Alpha Epsilon Delta, Beta Beta Beta Biological Honor Society, the Filipino Student Association, and the Asian American Student Association.

Evaluating Large Language Models for Accurate Lab Test Question Interpretation

Authors: Angelique Deville, Dr. Zhe He
Student Major: Biological Sciences
Mentor: Dr. Zhe He
Mentor's Department: School of Information
Mentor's College: College of Communication & Information
Co-Presenters: Hailey Thompson & Caroline Bennett

Abstract


The LabGenie project aims to address the challenge of patients, especially the elderly, in understanding medical lab test results and acting upon them. Even though generative AI models such as ChatGPT can answer questions, patients may not know what questions to ask, and they may also generate answers with inaccurate information or hallucinations. In the eHealth Lab, we are developing informatics strategies to augment large language models (LLM) by 1) identifying credible health sources for lab test result interpretation, and 2) curating these sources to computable format. As such, they can be used for question prompt enrichment with human input and retrieval-augmented generation (RAG). The ultimate goal is to integrate the RAG-based LLMs with a user-friendly patient interface. LabGenie seeks to allow patients with low health literacy to ask contextualized questions and confidently make informed health decisions with their providers. The research involves literature reviews on LLM capabilities in clinical settings, converting lab result interpretation into a table format, and evaluating the strengths and weaknesses of different LLMs in answering lab result questions. These procedures aim to provide meaningful results to train LLMs and contribute to the creation of LabGenie.

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Keywords: Large Language Model, eHealth, Artificial Intelligence, Lab Test