Summary of Multimodal Machine Learning in Mental Health: a Survey Of Data, Algorithms, and Challenges, by Zahraa Al Sahili et al.
Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges
by Zahraa Al Sahili, Ioannis Patras, Matthew Purver
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Emerging Technologies (cs.ET)
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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 This paper explores the application of machine learning (ML) in detecting, diagnosing, and treating mental health disorders by combining information from multiple modalities, such as text, audio, and video. The multimodal approach has shown promise in recognizing mental health symptoms and risk factors, offering novel insights into human behavior patterns. However, the field is still emerging, facing challenges before practical applications can be developed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning can help detect and treat mental health problems by using lots of different types of data, like what people say, how they talk, and even videos of them interacting with others. So far, this type of approach has shown that it’s possible to learn more about human behavior and recognize signs of mental health problems. But there are still many challenges to overcome before this can be used in real-life situations. |
Keywords
* Artificial intelligence * Machine learning
