Journal of Neurological, Psychiatric and Mental Health Nursing

Machine Learning Approaches for Mental Health Relapse Prediction: Toward Clinical Deployment

Suresh Mohanrao Renge — 2026-05-01

To some extent, the study was successful in realising the goal of precision psychiatry using ML and multi-modal features for the prediction of relapse in psychosis, bipolar and depressive disorders, with AUC between 0.69 and 0.89 as evidenced in. However, based on the problems stated in the systematic review, it found that only 18% of the models in the systematic review have a methodology that could be rated acceptable for the use of external validation, workflow/operationalization, transparency and generalizability. This work proposes a novel TRIPOD-compliant pipeline that implements XGBoost, Random Forests and Deep Neural Networks using SHAP/LIME explainers for explainability of the model. It uses the FHIR standard to handle more than 500 features comprising EHR features, digital biomarkers, and NLP features. It aims to answer questions beyond just a high predictive power and look to, among other issues, address federated learning for site-independent generalizability, high-speed inference on edge devices (within 1s), and identify the patients at highest risk. This paper plans to extend on the existing state-of-the-art in the area of relapse prediction to validate the model on a phase III RCT including more than 1,500 patients and later perform validation on a multi-ethnic and independent dataset, followed by a potential deployment. The system also focuses on external validation, calibration, and ethical implementation, which are significant translational challenges in precision psychiatry. The data is a summary of pooled results and design assumptions from previously published, validated research with intended implementation in real-life cohorts.

Early Behavioural Markers of Autism Spectrum Disorder: A Narrative Review

Amit Das — 2026-05-27

Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by deficits in social communication and restricted, repetitive behaviours. Early identification of behavioural markers is critical for timely intervention and improved developmental outcomes.

Methods: A narrative review was conducted using electronic databases, including PubMed/MEDLINE, CINAHL, PsycINFO, and Google Scholar, covering publications from 2010 to 2024. Studies focusing on behavioural markers of ASD in children aged 0–36 months were included. A total of 17 peer-reviewed articles, encompassing prospective, retrospective, observational, screening-based, and review designs, were analyzed. Data were extracted on study characteristics, tools used, and key findings, and synthesized thematically.

Results: Early behavioural markers of ASD clustered into five domains: impaired social orienting and joint attention, atypical language and communication development, restricted and repetitive behaviours, sensory processing abnormalities, and reduced social smiling and affective responsiveness. These markers were identifiable as early as 6–12 months, particularly among high- risk infants. Screening and observational tools such as M-CHAT-R/F, AOSI, CSBS, and SACS demonstrated utility in early detection across clinical and community settings.

Conclusion: Early behavioural markers of ASD can be reliably identified prior to the conventional diagnostic age of 24–36 months. Nurses and primary healthcare providers play a critical role in developmental surveillance, caregiver education, and early referral. Integration of standardized screening tools into routine child health services is essential to enhance early detection and intervention outcomes.

Violence Against Girl Children: A Comprehensive Review on Causes, Consequences, Prevention, and the Role of Healthcare Professionals

Subrahmanya Nayak — 2026-05-29

Violence against girl children is a major global public health and human rights concern affecting physical, psychological, social, and educational well-being. Girls are vulnerable to physical abuse, sexual violence, emotional abuse, trafficking, cyber exploitation, and neglect. This review article examines the magnitude, risk factors, consequences, legal safeguards, preventive strategies, and the role of healthcare professionals in protecting girl children. Literature was reviewed from databases including PubMed, Google Scholar, WHO publications, UNICEF reports, and governmental reports. The review identified multiple contributing factors, such as gender inequality, poverty, domestic violence, substance abuse, social stigma, and increasing digital vulnerabilities. Violence against girls leads to depression, anxiety, post-traumatic stress disorder, poor academic performance, social withdrawal, and long-term trauma. Legal frameworks, including the Protection of Children from Sexual Offences (POCSO) Act, play a significant role in child protection; however, underreporting and inadequate implementation remain major challenges. Nurses and healthcare professionals contribute through early identification, mandatory reporting, counselling, rehabilitation, and community awareness programs. Preventive strategies involving families, schools, healthcare systems, communities, and policymakers are essential to ensure the safety, dignity, and empowerment of girl children.