Machine Learning and Deep Learning Applications in Early Detection and Management of Mental Health Disorders
A comprehensive survey reviews how machine learning and deep learning technologies are being applied to early identification, diagnosis, and treatment of mental health conditions like depression, bipolar disorder, and schizophrenia. The research examines applications across medical imaging, genetic analysis, biomarkers, and behavioral assessments, along with predictive modeling for disease risk. The findings highlight both the potential to improve diagnostic accuracy and treatment outcomes, as well as significant challenges around data integration, methodological consistency, and ethical implementation.
This arXiv preprint survey examines the growing role of machine learning and deep learning in mental health care, focusing on early detection and management of disorders including depression, bipolar disorder, and schizophrenia. The review covers multiple data modalities—medical imaging, genetic information, biomarkers, and behavioral assessments—and discusses how these technologies can enhance clinical outcomes through improved diagnostic accuracy and personalized treatment approaches. The authors identify key challenges including data integration across heterogeneous sources, methodological inconsistencies in existing research, and ethical concerns around implementation. The survey emphasizes the importance of developing real-time monitoring systems for individualized care, advancing data fusion techniques, and fostering interdisciplinary collaboration. Future research directions focus on overcoming these obstacles to enable responsible and effective deployment of ML and DL technologies in clinical mental health settings.
What's missing
The survey does not provide specific performance metrics (sensitivity, specificity, accuracy rates) for the ML/DL methods reviewed, nor does it detail which specific algorithms or architectures showed the strongest results. Additionally, the current state of clinical adoption and regulatory approval status of these technologies is not discussed.
What different sources said
- arXiv cs.AICenter
Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning
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