AI Article Synopsis
- The text discusses the need to identify signs of Wellness Dimensions (WD) in self-narrated content amid a health crisis.
- It introduces a new data augmentation method using prompt-based Generative NLP models to balance social media data and enhance classification tasks for WD.
- The results show that the ChatGPT model outperforms other techniques, achieving significant improvements in evaluation metrics like F-scores and Matthew's Correlation Coefficient.
Article Abstract
Amid ongoing health crisis, there is a growing necessity to discern possible signs of Wellness Dimensions (WD) manifested in self-narrated text. As the distribution of WD on social media data is intrinsically imbalanced, we experiment the generative NLP models for data augmentation to enable further improvement in the pre-screening task of classifying WD. To this end, we propose a simple yet effective data augmentation approach through prompt-based Generative NLP models, and evaluate the ROUGE scores and syntactic/semantic similarity among and . Our approach with ChatGPT model surpasses all the other methods and achieves improvement over baselines such as Easy-Data Augmentation and Backtranslation. Introducing data augmentation to generate more training samples and balanced dataset, results in the improved F-score and the Matthew's Correlation Coefficient for upto 13.11% and 15.95%, respectively.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10878427 | PMC |
| http://dx.doi.org/10.18653/v1/2023.bionlp-1.27 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Testing the Feasibility, Acceptability, and Potential Efficacy of an Innovative Digital Mental Health Care Delivery Model Designed to Increase Access to Care: Open Trial of the Digital Clinic.
JMIR Ment Health
January 2025
Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.
Background: Mental health concerns have become increasingly prevalent; however, care remains inaccessible to many. While digital mental health interventions offer a promising solution, self-help and even coached apps have not fully addressed the challenge. There is now a growing interest in hybrid, or blended, care approaches that use apps as tools to augment, rather than to entirely guide, care.
View Article and Find Full Text PDFScrub typhus association with autoimmune biomarkers and clinical implications.
PLoS Negl Trop Dis
January 2025
Department of Internal Medicine, Jeonbuk National University Medical School, Jeonju, Republic of Korea.
Background: Scrub typhus, a disease caused by Orientia tsutsugamushi, triggers systemic vasculitis and is prevalent in Eastern and Southern Asia. This study aimed to uncover the relationship between scrub typhus and autoimmune responses, focusing on antinuclear antibodies (ANAs) and the implications of elevated ANA titers during infection.
Method: Data from a total of 139 patients diagnosed with scrub typhus and 30 healthy controls were retrospectively analyzed through serum samples to assess the levels of ANAs and related autoantibodies.
Assessing the sustainable development in the European Union: influence of municipal waste, industrial waste, and waste related patents.
Integr Environ Assess Manag
January 2025
Department of Economics, Hatay Mustafa Kemal University, Hatay, Turkey.
Waste has emerged as a pressing concern for the environment, primarily stemming from the processes of urbanization and industrialization. The substantial volumes of waste generated pose a serious threat to the environment, as they spread out harmful substances in the soil and release methane emissions into the atmosphere. To effectively address this issue, this study explores the impact of municipal and industrial waste, as well as waste-related innovation on the load capacity factor (LCF) from 2005 to 2020.
View Article and Find Full Text PDFscHNTL: single-cell RNA-seq data clustering augmented by high-order neighbors and triplet loss.
Bioinformatics
January 2025
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Sichuan 611756, China.
Motivation: The rapid development of single-cell RNA sequencing (scRNA-seq) has significantly advanced biomedical research. Clustering analysis, crucial for scRNA-seq data, faces challenges including data sparsity, high dimensionality, and variable gene expressions. Better low-dimensional embeddings for these complex data should maintain intrinsic information while making similar data close and dissimilar data distant.
View Article and Find Full Text PDFGenerative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
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!