The Role of AI in Personalized Mental Health Interventions
Abstract
Mental health disorders require personalized treatment approaches, yet traditional methods often fail to account for individual variations. This paper investigates how AI can enhance mental health interventions through personalized therapy recommendations, sentiment analysis, and predictive analytics. Using deep learning models trained on patient data, we develop an AI-driven framework that identifies early signs of mental health decline and suggests tailored interventions. Experimental results highlight the model’s ability to improve patient outcomes by predicting therapy effectiveness and adjusting recommendations in real-time. Challenges related to data privacy, model bias, and ethical considerations are discussed.
References
Balantrapu, S. S. (2021). The Impact of Machine Learning on Incident Response Strategies. International Journal of Management Education for Sustainable Development, 4(4), 1-17.
Balantrapu, S. S. (2019). Adversarial Machine Learning: Security Threats and Mitigations. International Journal of Sustainable Development in Computing Science, 1(3), 1-18.
Pillai, S. E. V. S., Polimetla, K., Avacharmal, R., & Perumal, A. P. (2022). Mental health in the tech industry: Insights from surveys and NLP analysis. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 10(2), 22-33.
Deekshith, A. (2022). Cross-Disciplinary Approaches: The Role of Data Science in Developing AI-Driven Solutions for Business Intelligence. International Machine learning journal and Computer Engineering, 5(5).
Deekshith, A. (2021). Data Engineering for AI: Optimizing Data Quality and Accessibility for Machine Learning Models. International Journal of Management Education for Sustainable Development, 4(4), 1-33.
Deekshith, A. (2023). Scalable Machine Learning: Techniques for Managing Data Volume and Velocity in AI Applications. International Scientific Journal for Research, 5(5).
Deekshith, A. (2020). AI-Enhanced Data Science: Techniques for Improved Data Visualization and Interpretation. International Journal of Creative Research In Computer Technology and Design, 2(2).
Deekshith, A. (2019). Integrating AI and Data Engineering: Building Robust Pipelines for Real-Time Data Analytics. International Journal of Sustainable Development in Computing Science, 1(3), 1-35.
Pillai, S. E. V. S., Polimetla, K., Avacharmal, R., Perumal, A. P., & Gopal, S. K. (2023). Beyond the Bin: Machine Learning-Driven Waste Management for a Sustainable Future. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 11(1), 16-27.
Boppiniti, S. T. (2023). Data Ethics in AI: Addressing Challenges in Machine Learning and Data Governance for Responsible Data Science. International Scientific Journal for Research, 5(5).
Boppiniti, S. T. (2020). Big Data Meets Machine Learning: Strategies for Efficient Data Processing and Analysis in Large Datasets. International Journal of Creative Research In Computer Technology and Design, 2(2).
Boppiniti, S. T. (2021). Real-Time Data Analytics with AI: Leveraging Stream Processing for Dynamic Decision Support. International Journal of Management Education for Sustainable Development, 4(4).
Boppiniti, S. T. (2019). Machine Learning for Predictive Analytics: Enhancing Data-Driven Decision-Making Across Industries. International Journal of Sustainable Development in Computing Science, 1(3).
