A5092172046- Suicide and Self-Harm Studies
- Image Retrieval and Classification Techniques
- Heart Rate Variability and Autonomic Control
- Psychosomatic Disorders and Their Treatments
- Multimodal Machine Learning Applications
- Spectroscopy and Chemometric Analyses
- Advanced Image and Video Retrieval Techniques
- Mental Health Treatment and Access
- Artificial Intelligence in Healthcare
- AI in cancer detection
- Child and Adolescent Psychosocial and Emotional Development
Shenzhen University
2023-2025
Pennsylvania State University
2024
Background Adolescents with depression are at heightened risk of suicide, a distinct sex difference in suicidal behaviour observed. This study explores the sex-specific factors influencing suicide attempts among Chinese adolescents depression.
We present MOFI, Manifold OF Images, a new vision foundation model designed to learn image representations from noisy entity annotated images. MOFI differs previous work in two key aspects: (i) pre-training data, and (ii) training recipe. Regarding we introduce approach automatically assign labels images image-text pairs. Our involves employing named recognition extract entities the alt-text, then using CLIP select correct as of paired image. It's simple, cost-effective method that can scale...
This study investigates the application of machine learning (ML) algorithms in early diagnosis breast cancer, focusing on logistic regression and Support Vector Classification (SVC). Utilizing a dataset from Kaggle, which includes diverse clinical features mass samples, research conducts comparative analysis these models terms accuracy interpretability. Our findings reveal that both SVC demonstrate high precision distinguishing between benign malignant tumors, with showing marginally...
Predicting non-suicidal self-injury (NSSI) among adolescents is challenging due to its complex behavioral drivers and diverse predictors. Machine learning (ML) methods, though promising in various predictions, have been underexplored for NSSI adolescents, particularly with large-scale cross-sectional datasets. In this study, we compiled 30 potential predictive variables from existing research used psychological survey data 2,343 participants across 14 Chinese psychiatry hospitals. The...