Date of Award
2024
Document Type
Open Access Master's Thesis
Degree Name
Master of Science in Health Informatics (MS)
Administrative Home Department
Department of Applied Computing
Advisor 1
Guy Hembroff
Committee Member 1
Ronghua Xu
Committee Member 2
Shane Meuller
Abstract
With advancements in technology and the growth of artificial intelligence, the gap between healthcare challenges and solutions is narrowing. This study explores the use of large language models (LLMs) to screen for Alzheimer's disease through patient-physician conversations. Fine-tuning and instruction-tuning techniques were applied to improve predictions. The performance of fine-tuned open-source models like LLaMA and GPT-3 was compared to smaller models such as TinyLLaMA and retrieval-augmented generation (RAG) models. The models were tested to determine their ability to classify individuals as cognitively normal (CN) or at any stage of Alzheimer's disease (AD). Reinforcement learning through human feedback was used to evaluate the models, while the DementiaBank dataset validated the evaluation metrics. This research demonstrates the potential of LLMs in advancing mental health diagnostics.
Recommended Citation
Addepalli, Venkatanand ram, "EARLY SCREENING OF ALZHEIMER’S DISEASE THROUGH VERBAL COMMUNICATION AND USING LARGE LANGUAGE MODELS (LLMS)", Open Access Master's Thesis, Michigan Technological University, 2024.