Machine Learning based Patient Mental Health Prediction using Spectral Clustering and RBFN Algorithms

In 2004, the American Academy of Pediatrics (AAP) Board of Directors formed the Task Force on Mental Health and charged it with developing strategies to improve the quality of child and adolescent mental health* services in primary care. The task force acknowledged early in its deliberations that enhancing the mental health care that pediatricians and other primary care clinicians† provide to children and adolescents will require systemic interventions at the national, state, and community levels to improve the financing of mental health care and access to mental health specialty resources. Systemic strategies toward achieving these improvements are the subject of other publications of the task force: “ Strategies for System Change in Children's Mental Health: A Chapter Action Kit ” (chapter action kit),1 “Improving Mental Health Services in Primary Care: Reducing Administrative and Financial Barriers to Access and Collaboration,”2 and “Enhancing Pediatric Mental Health Care: Strategies for Preparing a Community.”3 The task force also recognized that enhanced mental health practice will require competencies not currently achieved by many primary care clinicians; in the policy statement “The Future of Pediatrics: Mental Health Competencies for Pediatric Primary Care,”4 the task force collaborated with the AAP Committee on Psychosocial Aspects of Child and Family Health to outline these competencies and propose strategies for achieving them. This report offers strategies for preparing the primary care practice itself for provision of enhanced mental health care services. The task force proposes incrementally applying chronic care principles to the care of children with mental health and substance abuse problems as primary care clinicians apply them to the care of children with chronic medical conditions such as asthma. Most primary care clinicians will find that significant gaps exist between their current practice and the proposed ideal. The task force offers guidance in … Address correspondence to Jane Meschan Foy, MD, Department of Pediatrics, Wake Forest University School of Medicine, Medical Center Blvd, Winston-Salem, NC 27157. E-mail: jmfoy{at}wfubmc.edu

The central place of psychiatry in health care worldwide.

10.1176/appi.ps.59.1.105

Background: Adolescent depression is highly prevalent in low- and middle-income countries (LMICs). Identifying top key risk factors is necessary to inform effective prevention program design. Machine learning (ML) offers a powerful approach to analyze complex multidomain of data to identify the most relevant predictors and estimate risks for mental health problems. This paper applies ML to study risks for adolescent depression to enhance adolescent depression prevention efforts in LMICs. Methods: Six ML approaches (e.g., Explainable Boosting Machine, random forests, and XGBoost) were applied to study the risks of depression. Data were drawn from a digital health intervention study conducted in Kenya (year 2024–2025, n = 269). Multiple domains of childhood and adolescent adversity and stress experiences were used to predict adolescent depression (using PHQ9-A). Findings: We found that ML was a valuable approach in the early identification of adolescents at risk for depression. Among the six ML approaches applied, the random forest approach outperformed other ML approaches, especially when multiple domains of risks were included. We also found that childhood adversity or home adversity alone were not strong predictors for depression. Adding adolescent stress experiences and community school adversity experiences significantly improves the accuracy and predictability of depression. Using the top-15 and top-20 ranking factors, we achieved 74.8% and 75.1% accuracy in depression prediction, which was similar to the accuracy when all 49 adverse/stress factors were included in the predictive model (78.3%). Conclusions: Innovative ML and modern predictive modeling approaches have the potential to transform modern preventive mental health care by better utilizing multidomain data to identify individuals at risk for developing depression early and identify top risk factors (for targeted individuals and/or populations). Findings from ML can inform tailored intervention design to better mitigate risks in order to prevent depression problem development. They can also inform the better utilization of resources to target high-need cases and key determinants, which is particularly useful for LMICs and low-resource settings. This paper illustrates an example of how to move toward this direction. Future research is needed to validate the approach.

The Link Between Homeless Women's Mental Health and Service System Use

County-Level Estimates of Need for Mental Health Professionals in the United States

Information about mental health systems is essential for mental health planning to reduce the burden of neuropsychiatric disorders. Unfortunately, many low- and middle-income countries lack systematic information on their mental health systems. The objectives, scope, structure, and contents of mental health assessment and monitoring instruments commonly used in high-income countries may not be appropriate for use in middle- and low-income countries. The World Health Organization (WHO) has recently developed the WHO Assessment Instrument for Mental Health Systems (WHO-AIMS), a comprehensive assessment tool for mental health systems designed for middle- and low-income countries. WHO-AIMS was developed through an iterative process that included input from in-country and international experts on the clarity, content, validity, and feasibility of the instrument, as well as a pilot trial. The resulting instrument, WHO-AIMS 2.2, consists of six domains: policy and legislative framework, mental health services, mental health in primary care, human resources, public information and links with other sectors, and monitoring and research. These domains address the ten recommendations of the World Health Report 2001 through 28 facets and 155 items. All six domains need to be assessed to form a basic, yet broad, picture of a mental health system, with a focus on health sector activities. WHO-AIMS provides essential information for mental health policy and service delivery. Countries will be able to develop information-based mental health policy and plans with clear baseline information and targets. Moreover, they will be able to monitor progress in implementing reform policies, providing community services, and involving consumers, families, and other stakeholders in mental health promotion, prevention, care and rehabilitation. This article provides an overview of the rationale, development process, and potential uses and benefits of WHO-AIMS.

BackgroundAnxiety is a pervasive mental health disorder with severe implications for individual wellbeing and societal productivity. The contemporary rise of anxiety, particularly among youth in digitally-saturated environments, underscores a critical need for advanced predictive tools to facilitate early intervention and mitigation. While machine learning (ML) holds significant promise in this domain, a comprehensive synthesis of its application in anxiety prediction, along with a critical evaluation of methodological trends and gaps, is only emerging in the literature. The main idea of the current systematic review is to bridge the understanding of current ML applications in mental health with the critical needs for enhanced diagnostic precision, personalized interventions and prevention.ObjectivesThis systematic review aims to systematically synthesize research on ML approaches to predicting anxiety, critically evaluating the algorithms, features, and validation techniques employed across studies. The objective is to identify prevailing ML techniques, assess their performance, and highlight crucial methodological trends, existing gaps, and their implications for effective early intervention and real-world deployment.Eligibility criteriaStudies included had to apply machine learning techniques to predict anxiety or its severity using either clinical or behavioral datasets. Exclusion criteria included non-English language papers, reviews, older or previously reviewed publications, and those not specifically targeting anxiety. We focus on questionnaire research, but also discuss multimodal fusion techniques.Information sourcesWe searched the Scopus database and Google Scholar for articles published between 2018 and 2025 using combinations of keywords including “anxiety prediction,” “machine learning,” and “mental health.” The last search was conducted in July 2025.Risk of biasStudies were screened in two phases: (1) by verifying the presence of relevant keywords in the main body, and (2) by reviewing title, introduction, and conclusion to ensure alignment with anxiety prediction via ML. Studies relying solely on self-reported metrics or with unclear algorithmic transparency were noted for potential bias.ResultsA total of 19 studies were included, encompassing 44, 608 participants. GAD-7 and DASS-21 were the most commonly used diagnostic instruments. ML techniques such as Random Forest and Gradient Boosting achieved the highest predictive accuracy, with some studies reporting up to 98% accuracy. Metrics like F1-score, AUC, and specificity were commonly reported.Limitations of evidenceExisting studies display a range of methodological and conceptual limitations that constrain their generalizability and clinical utility. The review identified significant methodological limitations hindering generalizability and clinical utility, including reliance on small, homogeneous samples, which raises concerns about overfitting and population bias. Furthermore, common issues include a lack of external validation, inconsistent evaluation metrics, and the “black-box” nature of many ML algorithms, which impedes clinical trust and adoption.InterpretationThe findings support the effectiveness of machine learning for anxiety detection and prediction, particularly in early intervention contexts. The integration of explainable ML and diverse, clinically validated data is necessary for real-world deployment. The existing body of research also shows a notable scarcity in studies predicting anxiety before symptom manifestation. These insights emphasize the critical need for integrating explainable ML (XAI) and utilizing diverse, clinically validated datasets to enable real-world deployment and proactive mental health support.

Objective: This study aimed to investigate the trajectories of internalizing and externalizing problems following childhood potentially traumatic events (PTEs) and analyse a comprehensive set of baseline variables (PTEs, individual, environmental) to elucidate their predictive role as contributors to different mental health trajectories. Method: The sample consisted of 4141 participants (M = 9.48, SD = 0.51 years at baseline; 48.7% girls; 72.1% White) from the Adolescent Brain Cognitive Development study who had experienced at least one PTE. Participants’ mental health problems were assessed using the Brief Problem Monitor self-report form. Latent Growth Mixture Modelling was used to identify trajectories of youth´s internalizing and externalizing problems across the six assessments. Extreme Gradient Boosting, a machine learning approach, was utilized to investigate 37 predictors of different trajectories. Results: Three distinct trajectories were identified: “Resilient”, “Mild stable” and “Moderate chronic increasing”, for internalizing and “Resilient”, “Mild increasing” and “Moderate chronic decreasing” for externalizing problems. Predictors of the “Moderate chronic” versus “Resilient” trajectories were identified using machine learning. The three most important predictors of the internalizing problems trajectory were: behavioural inhibition, female gender, and less parental monitoring, whereas predictors of the externalizing problems trajectory were family conflicts, screentime and behavioural inhibition. Conclusion: The findings can help characterize individual variation in mental health trajectories following childhood PTEs and provide potential targets for intervention to foster mental health.

The Role of Providers in Mental Health Services Offered to American-Indian Youths

This study examined rates of utilization of mental health care among active duty and National Guard soldiers with mental health problems three and 12 months after they returned from combat in Iraq. Stigma and barriers to care were also reported for each component (active duty and National Guard). Cross-sectional, anonymous surveys were administered to 10,386 soldiers across both time points and components. Mean scores from 11 items measuring stigma and barriers to care were computed. Service utilization was assessed by asking soldiers whether they had received services for a mental health problem from a mental health professional, a medical doctor, or the Department of Veterans Affairs in the past month. Risk of mental problems was measured using the Patient Health Questionnaire, the PTSD Checklist, and items asking about aggressive behaviors and "stress, emotional, alcohol, or family" problems within the past month. A higher proportion of active duty soldiers than National Guard soldiers reported at least one type of mental health problem at both three months (45% versus 33%) and 12 months (44% versus 35%) postdeployment. Among soldiers with mental health problems, National Guard soldiers reported significantly higher rates of mental health care utilization 12 months after deployment, compared with active duty soldiers (27% versus 13%). Mean stigma scores were higher among active duty soldiers than among National Guard soldiers. Active duty soldiers with a mental health problem had significantly lower rates of service utilization than National Guard soldiers and significantly higher endorsements of stigma. Current and future efforts to improve care for veterans should work toward reducing the stigma of receiving mental health care.

Perceived Effectiveness of Medications Among Mental Health Service Users With and Without Alcohol Dependence

Global mental health and HIV care: gaps and research priorities.

To compare children with mental and physical health problems regarding (1) perceived disease severity; (2) the impact of their condition on their families; (3) their utilization of health care services (including satisfaction with care); and (4) parents' health literacy about their child's condition and its treatment. Furthermore, we examined whether parents' health literacy differs between types of mental health condition. Parental reports about their 9- to 14-year-old children with mental (n=785) or physical health problems (n=475) were analyzed from the population-based National Survey of Children with Special Health Care Needs in Switzerland. Mental health problems were perceived as being more severe (p<0.001) and exerting a larger impact upon the family (e.g., financial impact) than physical health problems. Furthermore, fewer parents of children with a mental health problem mentioned having a particular person or place to contact if they needed information or advice regarding the child's condition (p=0.004) and were satisfied with the health care services their child received (p<0.001). The odds of low health literacy was higher among parents with children suffering from mental health problems vs. parents of children with physical health problems (OR in the adjusted model=1.92; 95% CI 1.47-2.50; p<0.001); this finding held generally for mental health problem (although only a trend was observable for internalizing problems). The large impact of children's mental health conditions on themselves and their families might be reduced by adapting the provision of health care and by increasing parents' health literacy.

Towards Using Graph Analytics for Tracking Covid-19.