ArticleJournal of personalized medicine2024
Early Detection of Mental Health Crises through Artifical-Intelligence-Powered Social Media Analysis: A Prospective Observational Study.
Article in Journal of personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring global efforts in mental health screening and early detection.Frontiers in psychiatry · 2026Pooled it
- Between Help and Harm: An Evaluation Study of Mental Health Crisis Handling by Large Language Models.JMIR mental health · 2026Article
- MultiMindNet: AI-based mental health analysis using hybrid deep learning approach and Hybrid Ant-Grey Wolf Optimization (HAGWO) algorithm.PLOS digital health · 2026Article
- Understanding mental health discourse on Reddit with transformers and explainability.Scientific reports · 2026Article
- Reddit and rare diseases: what myositis communities tell us about support and struggle.Oxford open digital health · 2026Article
- Explainable Machine Learning in the Prediction of Depression.Diagnostics (Basel, Switzerland) · 2025Article
- Extreme Weather, Vulnerable Populations, and Mental Health: The Timely Role of AI Interventions.International journal of environmental research and public health · 2025Article
- Deep Learning-Based Detection of Depression and Suicidal Tendencies in Social Media Data with Feature Selection.Behavioral sciences (Basel, Switzerland) · 2025Article
- Developing a suicide risk prediction model for hospitalized adolescents with depression in China.Frontiers in psychiatry · 2025Article
- Current status and challenges of artificial intelligence application in managing Children's emotions and attention.Digital healthReview
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Authors and funding
2 authors.
Funding
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Abstract
backgroundThe early detection of mental health crises is crucial for timely interventions and improved outcomes. This study explores the potential of artificial intelligence (AI) in analyzing social media data to identify early signs of mental health crises.
methodsWe developed a multimodal deep learning model integrating natural language processing and temporal analysis techniques. The model was trained on a diverse dataset of 996,452 social media posts in multiple languages (English, Spanish, Mandarin, and Arabic) collected from Twitter, Reddit, and Facebook over 12 months. Its performance was evaluated using standard metrics and validated against expert psychiatric assessments.
resultsThe AI model demonstrated a high level of accuracy (89.3%) in detecting early signs of mental health crises, with an average lead time of 7.2 days before human expert identification. Performance was consistent across languages (F1 scores: 0.827-0.872) and platforms (F1 scores: 0.839-0.863). Key digital markers included linguistic patterns, behavioral changes, and temporal trends. The model showed varying levels of accuracy for different crisis types: depressive episodes (91.2%), manic episodes (88.7%), suicidal ideation (93.5%), and anxiety crises (87.3%).
conclusionsAI-powered analysis of social media data shows promise for the early detection of mental health crises across diverse linguistic and cultural contexts. However, ethical challenges, including privacy concerns, potential stigmatization, and cultural biases, need careful consideration. Future research should focus on longitudinal outcome studies, ethical integration of the method with existing mental health services, and developing personalized, culturally sensitive models.
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