Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
A family-based behavioral group obesity randomized control feasibility trial across a clinical trials network: a focus on contact hours.
J Pediatr Psychol
January 2025
Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, United States.
Objective: This ancillary study's purpose is to describe the relationship between dose of treatment and body mass index (BMI) outcomes in a tele-behavioral health program delivered in the IDeA States Pediatric Clinical Trials Network to children and their families living in rural communities.
Methods: Participants randomized to the intervention were able to receive 26 contact hours (15 hr of group sessions and 11 hr of individual sessions) of material focused on nutrition, physical activity, and behavioral caregiver training delivered via interactive televideo. Dose of the intervention received by child/caregiver dyads (n = 52) from rural areas was measured as contact hours.
A Comparison of Different Machine Learning Classifiers in Predicting Xerostomia and Sticky Saliva due to Head and Neck Radiotherapy using a Multi-objective, Multimodal Radiomics Model.
Biomed Phys Eng Express
January 2025
Radiation Oncology, Emory University, Emory Midtown Hospital, Atlanta, Georgia, 30322, UNITED STATES.
Although radiotherapy techniques are the primary treatment for head and neck cancer (HNC), they are still associated with substantial toxicity, and side effect. Machine learning (ML) based radiomics models for predicting toxicity mostly rely on features extracted from pre-treatment imaging data. This study aims to compare different models in predicting radiation-induced xerostomia and sticky saliva in both early and late stage of HNC patients using CT and MRI image features along with demographics and dosimetric information.
View Article and Find Full Text PDFEEG-derived brainwave patterns for depression diagnosis via hybrid machine learning and deep learning frameworks.
Appl Neuropsychol Adult
January 2025
Faculty Xavier Institute of Engineering, Mahim, India.
In the fields of engineering, science, technology, and medicine, artificial intelligence (AI) has made significant advancements. In particular, the application of AI techniques in medicine, such as machine learning (ML) and deep learning (DL), is rapidly growing and offers great potential for aiding physicians in the early diagnosis of illnesses. Depression, one of the most prevalent and debilitating mental illnesses, is projected to become the leading cause of disability worldwide by 2040.
View Article and Find Full Text PDFImpact of Online Interactive Decision Tools on Women's Decision-Making Regarding Breast Cancer Screening: Systematic Review and Meta-Analysis.
J Med Internet Res
January 2025
Cancer Screening, American Cancer Society, Atlanta, GA, United States.
Background: The online nature of decision aids (DAs) and related e-tools supporting women's decision-making regarding breast cancer screening (BCS) through mammography may facilitate broader access, making them a valuable addition to BCS programs.
Objective: This systematic review and meta-analysis aims to evaluate the scientific evidence on the impacts of these e-tools and to provide a comprehensive assessment of the factors associated with their increased utility and efficacy.
Methods: We followed the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and conducted a search of MEDLINE, PsycINFO, Embase, CINAHL, and Web of Science databases from August 2010 to April 2023.
Exploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review.
JMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.
View Article and Find Full Text PDFWant 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!