Summary of Explainable Ai For Mental Disorder Detection Via Social Media: a Survey and Outlook, by Yusif Ibrahimov et al.
Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook
by Yusif Ibrahimov, Tarique Anwar, Tommy Yuan
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 research surveys the intersection of data science, artificial intelligence, and mental healthcare, focusing on recent developments in detecting mental disorders through online social media (OSM). It reviews state-of-the-art machine learning methods, emphasizing the need for explainability in healthcare AI models. The paper navigates traditional diagnostic methods, AI-driven research studies, and XAI models for mental healthcare. It highlights the potential of OSM platforms as a vast repository of personal data for mental health analytics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mental health is a big problem that affects many people around the world. This paper looks at how we can use data science and artificial intelligence to help detect mental disorders on social media platforms. These platforms have a lot of information about people’s thoughts, feelings, and behaviors, which could be used to identify signs of mental illness. The paper talks about different ways that researchers are using AI to analyze this data and improve mental health care. It also discusses the importance of making sure these AI systems are transparent and easy to understand. |
Keywords
* Artificial intelligence * Machine learning
