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Journal of Mental Health Nursing & Statistics

Volume 2, Issue 1, 2026
Mcmed International
Journal of Mental Health Nursing & Statistics
Issn
3117-4345 (Print), 3117-4353 (Online)
Frequency
bi-annual
Email
editorJMHNS@mcmed.us
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Abstract
Title
BIG DATA AND MACHINE LEARNING APPROACHES IN MENTAL HEALTH NURSING: A STATISTICAL PERSPECTIVE FOR PATIENT-CENTERED CARE
Author
Dr. Mrutyunjaya M S
Email
keyword
Big Data, Machine Learning, Mental Health Nursing, Predictive Analytics, PatientCentered Care, Statistical Modeling, Depression Management, Nursing Informatics.
Abstract
Big data and machine learning (ML) are increasingly being recognized as transformative forces in the healthcare sector, particularly in mental health nursing. Unlike traditional nursing practices that rely heavily on clinical observation and qualitative assessments, big data enables the analysis of vast, complex, and diverse patient information collected from electronic health records (EHRs), wearable devices, patient surveys, and genetic profiles. When paired with advanced ML algorithms, these data can reveal patterns that are often invisible to human judgment alone, providing new opportunities for proactive, patientcentered care. This paper explores how mental health nurses can use predictive analytics to detect early warning signs of relapse, identify individualized treatment responses, and design evidence-based interventions. Statistical models such as logistic regression, random forest classifiers, and neural networks are evaluated for their ability to enhance decision-making processes. A case study focused on depression relapse prediction demonstrates the clinical utility of ML tools, while a structured nursing questionnaire highlights professional perspectives on technology adoption. The paper concludes that the future of mental health nursing lies in integrating human empathy with data-driven intelligence to deliver holistic, effective, and patient-specific care.
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