Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Utility of word embeddings from large language models in medical diagnosis.
J Am Med Inform Assoc
January 2025
Kennewick, WA 99338, United States.
Objective: This study evaluates the utility of word embeddings, generated by large language models (LLMs), for medical diagnosis by comparing the semantic proximity of symptoms to their eponymic disease embedding ("eponymic condition") and the mean of all symptom embeddings associated with a disease ("ensemble mean").
Materials And Methods: Symptom data for 5 diagnostically challenging pediatric diseases-CHARGE syndrome, Cowden disease, POEMS syndrome, Rheumatic fever, and Tuberous sclerosis-were collected from PubMed. Using the Ada-002 embedding model, disease names and symptoms were translated into vector representations in a high-dimensional space.
Background: The early diagnosis and monitoring of Alzheimer's disease (AD) presents a significant challenge due to its heterogeneous nature, which includes variability in cognitive symptoms, diagnostic test results, and progression rates. This study aims to enhance the understanding of AD progression by integrating neuroimaging metrics with demographic data using a novel machine learning technique.
Method: We used supervised Variational Autoencoders (VAEs), a generative AI method, to analyze high-dimensional neuroimaging data for AD progression, incorporating age and gender as covariates.
Biomarkers.
Alzheimers Dement
December 2024
Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Background: Uniform manifold approximation and projection (UMAP) is a technique for dimension reduction and visualization of high-dimensional (HD) data. Here, we apply UMAP to represent in two dimensions, data from members of the Wake Forest School of Medicine Alzheimer's Disease Research Center (WFUSM-ADRC) clinical cohort.
Methods: We examined baseline data from 542 WFUSM-ADRC participants with mean age 70.
Background: Studies have shown physical activity (PA) patterns are heritable traits and are correlated with several known genetic risk factors including APOE, the best-known gene associated with Alzheimer's Disease (AD). SPARE-AD was a previously developed machine learning index known to be sensitive to AD-like brain atrophy. However, the relationship between genetic variants, physical activity patterns and AD-related neuroimaging features have yet been extensively studied due to the lack of appropriate data and statistical methods for handling complex multimodal data.
View Article and Find Full Text PDFBackground: Screening and disease monitoring are two core challenges of disease management in Alzheimer's Disease (AD). Digital speech and language features have shown promise as clinical outcomes in related disorders.
Methods: We reviewed how speech digital markers are currently used in AD, focusing on how behaviours are connected to underlying disease pathology.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!