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Updated: Jun 6, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Exploring global efforts in mental health screening and early detection
Haya S Zedan1, Abeer Alshehri2, Mohammed M J Alqahtani3
1Department of Public Health, College of Health Sciences, Saudi Electronic University, Riyadh, Saudi Arabia.
Introduction:
Screening tools are frequently used to assess symptoms of mental disorders in different settings before they worsen. They may theoretically be used in large-scale programs to support implementation of national action plans and mental health strategies in consideration of broader or more specific lines of action for support of individuals struggling with mental health issues. Although screening tools and programs are generally effective, there are inconsistencies in their design, planning, application, measurement of outcomes, mechanisms used for follow-up, and ethical considerations. These inconsistencies highlight the need for benchmarking efforts to guide the identification, assessment, and development of the most appropriate screening tools and early detection programs.
Methods:
A scoping review of literature related to mental health screening and early detection program frameworks, standards, protocols, practices, tools, techniques from around the world and evidence of effectiveness.
Results:
Of the 2213 potential studies identified, 38 were selected for inclusion. The majority were from the USA (26.3%). Most studies utilized non-randomized study designs, and there was a wide range of settings, sample sizes, life stages, and mental disorders assessed. The most commonly used tools were the Patient Health Questionnaire (PHQ), Kessler Scale, ProfScreen, and Generalized Anxiety Disorder assessment (GAD). Screening tools were found generally effective, but sensitivity and specificity varied. The Posttraumatic Symptoms Scale (PTSS-10), AC-OK, and Adolescent Psychotic-Like Symptom Screener (APSS) showed high diagnostic performance (98%, 96%, and 68%, respectively). Challenges faced included ease of use, additional resource requirements, and technological accessibility. Limited studies performed cost-effectiveness analyses.
Conclusion:
No single mental health screening tool is universally superior; each tool has advantages and limitations. The effectiveness of these tools depends on the population, settings, application, and integration with other care pathways.
Recommendations:
Future studies should include in-depth evaluations of screening tools and cost-effectiveness analyses to facilitate better comparison for diverse populations and settings, as well as the design and development of appropriate tools and programs for screening and early detection.
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