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Engagement of adolescents with ADHD in a narrative-centered game-based behavior change environment to reduce alcohol use.
Front Educ (Lausanne)
January 2024
Division of Adolescent and Young Adult Medicine, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States.
Background: Attention deficit hyperactivity disorder (ADHD) affects about 13% of adolescents and is associated with substance use-related morbidity and mortality. While evidence on effective interventions to reduce alcohol use among adolescents with ADHD is limited, parent-teen communication about alcohol use has been found to be protective. Other approaches, such as educational interventions, hold promise to reduce alcohol-related harms in adolescents with ADHD.
View Article and Find Full Text PDFResearch on grading detection methods for diabetic retinopathy based on deep learning.
Pak J Med Sci
January 2025
Juan Chen, Department of Ophthalmology, Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China.
Objective: To design a deep learning-based model for early screening of diabetic retinopathy, predict the condition, and provide interpretable justifications.
Methods: The experiment's model structure is designed based on the Vision Transformer architecture which was initiated in March 2023 and the first version was produced in July 2023 at Affiliated Hospital of Hangzhou Normal University. We use the publicly available EyePACS dataset as input to train the model.
Background And Aims: This article explores antibiotic resistance as a critical public health issue in Bangladesh, emphasizing its growing threat to the nation's healthcare system. As a developing country, Bangladesh faces unique challenges in managing this crisis, with antibiotic resistance posing significant risks due to its multidimensional problems to both individual health and the broader population.
Methods: We searched for relevant pieces of literature that discuss the antibiotic resistance problem both from a global and national perspective.
A novel machine learning based framework for developing composite digital biomarkers of disease progression.
Front Digit Health
January 2025
Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ, United States.
Background: Current methods of measuring disease progression of neurodegenerative disorders, including Parkinson's disease (PD), largely rely on composite clinical rating scales, which are prone to subjective biases and lack the sensitivity to detect progression signals in a timely manner. Digital health technology (DHT)-derived measures offer potential solutions to provide objective, precise, and sensitive measures that address these limitations. However, the complexity of DHT datasets and the potential to derive numerous digital features that were not previously possible to measure pose challenges, including in selection of the most important digital features and construction of composite digital biomarkers.
View Article and Find Full Text PDFLiver fibrosis classification on trichrome histology slides using weakly supervised learning in children and young adults.
J Pathol Inform
January 2025
Cincinnati Children's AI Imaging Research (CAIIR) Center, Cincinnati, OH, United States.
Background: Traditional liver fibrosis staging via percutaneous biopsy suffers from sampling bias and variable inter-pathologist agreement, highlighting the need for more objective techniques. Deep learning models for disease staging from medical images have shown potential to decrease diagnostic variability, with recent weakly supervised learning strategies showing promising results even with limited manual annotation.
Purpose: To study the clustering-constrained attention multiple instance learning (CLAM) approach for staging liver fibrosis on trichrome whole slide images (WSIs) of children and young adults.
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