AI in Crisis Prediction and Prevention: Leveraging Predictive Analytics for Suicide Risk and Emotional Distress Management
1 Department of Health Informatics, Indiana University Indianapolis, Indiana, USA.
2 Department of Computer Science, Joseph Ayo Babalola University, Nigeria.
3 Dr Chetty Health Centre, Seychelles.
4 Federal Medical Centre, Abeokuta, Ogun State, Nigeria.
Review
International Journal of Biological and Pharmaceutical Sciences Archive, 2025, 10(02), 049-064.Article DOI: 10.53771/ijbpsa.2025.10.2.0078
Publication history:
Received on 05 September 2025; revised on 16 October 2025; accepted on 18 October 2025
Abstract:
The growing incidence of suicide and emotional distress across societies highlights an urgent public health challenge that requires effective and scalable preventive strategies. In recent years, artificial intelligence (AI) has emerged as a promising tool for enhancing crisis prediction and prevention through the use of predictive analytics. This review brings together current developments in the application of machine learning, natural language processing, and deep learning techniques that are capable of analyzing large and varied data sources, including electronic health records, social media activity, and data from wearable devices. These technologies enable the early identification of individuals at elevated risk of suicide or severe emotional distress with levels of precision that were previously unattainable. Beyond risk detection, AI-driven platforms are increasingly being integrated into mental health services to provide real-time support through digital companions, automated chat systems, and adaptive treatment recommendations that can be tailored to individual needs. Such innovations hold particular promise in regions with limited access to mental health professionals, offering a means to bridge gaps in care delivery. At the same time, the integration of AI into mental health care presents important challenges. Concerns about data representativeness, the interpretability of complex algorithms, and the transferability of models across diverse cultural and social settings need to be addressed to ensure fairness and reliability. Ethical considerations are equally pressing, particularly around safeguarding patient privacy, maintaining autonomy in care decisions, and building public trust in AI-assisted systems. The review emphasizes that while AI cannot replace human judgment in mental health care, it can serve as a powerful complement when developed within transparent and ethically sound frameworks. By fostering interdisciplinary collaboration among clinicians, data scientists, ethicists, and policymakers, AI-driven approaches can contribute to reducing suicide rates and mitigating emotional distress. In doing so, they pave the way for a more proactive, inclusive, and globally responsive mental health ecosystem.
Keywords:
Artificial Intelligence; Suicide Risk Prediction; Emotional Distress; Predictive Analytics; Machine Learning; Natural Language Processing; Mental Health; Ethical AI
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0