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Found 7 papers, 1 papers with code
Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection
On the depression detection task, our method (F1 = 0. 975~0. 978) significantly outperforms traditional supervised learning paradigms, including feature engineering (F1 = 0. 760) and architecture engineering (F1 = 0. 756).
Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model
To prevent widespread consequences, platforms are eager to predict these videos' impact on viewers' mental health.
Patient Dropout Prediction in Virtual Health: A Multimodal Dynamic Knowledge Graph and Text Mining Approach
In virtual health, the information asymmetries inherent in its delivery format, between different stakeholders, and across different healthcare delivery systems hinder the performance of existing predictive methods.
What Symptoms and How Long? An Interpretable AI Approach for Depression Detection in Social Media
This study contributes to IS literature with a novel interpretable deep learning model for depression detection in social media.
Depression Detection Using Digital Traces on Social Media: A Knowledge-aware Deep Learning Approach
Depression is a common disease worldwide.
Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT
To connect human expertise in the decision-making, safeguard trust for this high-stake prediction, and ensure algorithm transparency, we develop an interpretable deep learning model: Temporal Prototype Network (TempPNet).
Unbox the Blackbox: Predict and Interpret YouTube Viewership Using Deep Learning
Although deep learning champions viewership prediction, it lacks interpretability, which is fundamental to increasing the adoption of predictive models and prescribing measurements to improve viewership.
