Summary of Automated Multi-label Annotation For Mental Health Illnesses Using Large Language Models, by Abdelrahaman A. Hassan et al.
Automated Multi-Label Annotation for Mental Health Illnesses Using Large Language Models
by Abdelrahaman A. Hassan, Radwa J. Hanafy, Mohammed E. Fouda
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed methodology creates versatile multi-label datasets by cleaning, sampling, labeling, and combining social media posts to accurately diagnose and treat mental health disorders. A synthetic labeling technique transforms single-label datasets into multi-label annotations, capturing the complexity of overlapping conditions like depression and anxiety. The approach merges two single-label datasets into a foundational multi-label dataset, enabling realistic analyses of co-occurring diagnoses. Large language models are used with various prompting strategies to detect any present disorders. Optimal combinations are identified and applied to label six additional single-disorder datasets from RMHD. The result is SPAADE-DR, a robust, multi-label dataset encompassing diverse mental health conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand mental health by creating new ways to use social media posts. It’s like taking a puzzle with lots of pieces and making it into one big picture that shows how different mental health conditions are connected. The researchers took two sets of single-label data, merged them together, and added special labels to show how different disorders can happen at the same time. Then they used special computer models called large language models to figure out what’s going on in social media posts. They found the best way to use these models and applied it to six more datasets. The result is a super helpful tool that shows us how mental health conditions are connected, which will help us make better diagnoses and treatments. |
Keywords
» Artificial intelligence » Prompting
