Summary of Advancements in Machine Learning and Deep Learning For Early Detection and Management Of Mental Health Disorder, by Kamala Devi Kannan et al.
Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder
by Kamala Devi Kannan, Senthil Kumar Jagatheesaperumal, Rajesh N. V. P. S. Kandala, Mojtaba Lotfaliany, Roohallah Alizadehsanid, Mohammadreza Mohebbi
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The proposed survey reviews the development of machine learning (ML) and deep learning (DL) methods for early diagnosis and treatment of mental health issues, such as depression, bipolar disorder, and schizophrenia. It evaluates complex data from imaging, genetics, and behavioral assessments to improve clinical outcomes. The study discusses predictive modeling for illness progression, focusing on risk prediction models and longitudinal studies. Key findings highlight the potential benefits of ML and DL in improving diagnostic accuracy and treatment outcomes while addressing methodological inconsistencies, data integration challenges, and ethical concerns. The paper emphasizes the importance of real-time monitoring systems, data fusion techniques, and interdisciplinary collaboration to ensure the valuable and ethical application of ML and DL in mental health services. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how machine learning (ML) and deep learning (DL) can help doctors diagnose and treat mental health problems like depression and schizophrenia. It talks about using different types of data, such as brain scans, genetic information, and behavior assessments, to make better predictions about patient outcomes. The research also touches on the challenges of combining these different data sources and ensuring that ML and DL are used ethically in healthcare. |
Keywords
» Artificial intelligence » Deep learning » Machine learning
