Development of deep learning auto-encoder algorithms for predicting alcohol use in Korean adolescents based on cross-sectional data
Bibliographic Data
| ID | 4803333 |
|---|---|
| Authors | Serim Lee (0000-0003-3051-6625, Ewha Womans University, corresponding author) |
| Year | 2025 |
| Volume | 367 |
| Pages | 117690 |
| Publication date | 2025-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Science & Medicine (JOURNAL) |
| Journal identifiers | ISSN: 0277-9536 • E-ISSN: 1873-5347 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.socscimed.2025.117690 |
| PMID | 39892039 |
| OpenAlex | W4406250914 |
| Language | EN |
| Citations received | 1 |
| References cited | 36 |
Algorithm · Autoencoder · Cross-sectional study · Deep learning · Machine learning · Statistics · Computer Science · Health and Wellbeing Research · Mathematics · Medicine · Nutrition, Health and Food Behavior · Psychology · Technology and Data Analysis · Artificial Intelligence
Applied Predictive Modeling
Regularization and Variable Selection Via the Elastic Net
Prescription Opioid Misuse and Use of Alcohol and Other Substances Among High School Students — Youth Risk Behavior Survey, United States, 2019
Problematic smartphone use associated with greater alcohol consumption, mental health issues, poorer academic performance, and impulsivity
Overview and Methods for the Youth Risk Behavior Surveillance System — United States, 2019
Childhood and Adolescent Obesity
Utility of Machine-Learning Approaches to Identify Behavioral Markers for Substance Use Disorders
Identification of important features in overweight and obesity among Korean adolescents using machine learning
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |