Enhancing Early Detection of Cognitive Decline in the Elderly through Ensemble of NLP Techniques: A Comparative Study Utilizing Large Language Models in Clinical Notes

Presentation Time: 04:15 PM - 04:30 PM

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

This study aims to leverages large language models (LLMs) in secure clouds for a pioneering exploration of EHR note analysis for cognitive decline detection. Our findings indicate that LLMs and traditional models exhibit diverse error profiles. The ensemble of LLMs and locally trained machine learning models on EHR data was found to be complementary, enhancing performance and improving diagnostic accuracy, with a marked improvement in precision (from a 70%-79% range to above 90%).

Speaker(s):
Xinsong Du, Ph.D.
Brigham and Women's Hospital/Harvard Medical School

Author(s):
Xinsong Du, Ph.D. - Brigham and Women's Hospital/Harvard Medical School; John Laurentiev, MS; Joseph Plasek, PhD - Mass General Brigham; Ya-Wen Chuang, MD, MPH - BRIGHAM AND WOMEN'S HOSPITAL; Liqin Wang, PhD - Brigham and Women's Hospital; Surabhi Datta, PhD; Hunki Paek, PhD; Lin Bin, MS - Intelligence Medical Objects; Qiang Wei - The University of Texas Health Science at Houston; Xiaoyan Wang, PhD in Biomedical Informatics - MelaxTech; Jingqi Wang - Melax Technologies, Inc; Hao Ding, Ph.D. - Intelligent Medical Objects; Frank Manion, PhD - Intelligent Medical Objects; Jingcheng Du, Ph.D. - Melax Tech; Li Zhou, MD, PhD, FACMI, FIAHSI, FAMIA - Brigham and Women's Hospital, Harvard Medical School;