AIMC Topic: Mental Disorders
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Predicting the onset of mental health problems in adolescents.
Psychological medicine
Apr 30, 2025
OBJECTIVE: Mental health problems are the major cause of disability among adolescents. Personalized prevention may help to mitigate the development of mental health problems, but no tools are available to identify individuals at risk before they requ...
Trade-offs between machine learning and deep learning for mental illness detection on social media.
Scientific reports
Apr 25, 2025
Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation. Machine learning (ML) and deep learning (DL) models have been increas...
A cohort study of predictors of short-term nonfatal suicidal and self-harm events among individuals with mental health disorders treated in the emergency department.
Journal of psychiatric research
Apr 22, 2025
BACKGROUND: Patients presenting to emergency departments (EDs) for mental health problems have an elevated short-term risk of repeat ED visits, subsequent hospitalization, and suicide.
Fast and effective assessment for individuals with special needs form optimization and prediction models.
BMC psychology
Apr 22, 2025
The aim of this study was to determine which items in the psychological assessment forms used by counselling and research centres for individuals with special needs are effective in classifying individuals into special needs diagnostic categories. Da...
Machine learning based identification of suicidal ideation using non-suicidal predictors in a university mental health clinic.
Scientific reports
Apr 22, 2025
Suicide causes over 700,000 deaths annually worldwide. Mental disorders are closely linked to suicidal ideation, but predicting suicide remains complex due to the multifaceted nature of contributing factors. Traditional assessment tools often fail to...
Mental health practitioners' perceptions and adoption intentions of AI-enabled technologies: an international mixed-methods study.
BMC health services research
Apr 16, 2025
BACKGROUND: As mental health disorders continue to surge, exceeding the capacity of available therapeutic resources, the emergence of technologies enabled by artificial intelligence (AI) offers promising solutions for supporting and delivering patien...
An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.
Scientific reports
Apr 3, 2025
Classical approaches to diagnosis frequently rely on self-reported symptoms or clinician observations, which can make it difficult to examine mental health illnesses due to their subjective and complicated nature. In this work, we offer an innovative...
Validation of a generative artificial intelligence tool for the critical appraisal of articles on the epidemiology of mental health: Its application in the Middle East and North Africa.
Journal of epidemiology and population health
Apr 3, 2025
Mental health disorders have a high disability-adjusted life years in the Middle East and North Africa. This rise has led to a surge in related publications, prompting researchers to use AI tools like ChatGPT to reduce time spent on routine tasks. Ou...
Nanopsychiatry: Advancing psychiatric diagnosis and monitoring through nanotechnology-based detection.
Clinica chimica acta; international journal of clinical chemistry
Mar 26, 2025
Nanopsychiatry, operating at the nanoscale, leverages engineered nanomaterials and nanodevices to revolutionize psychiatric diagnostics and therapeutics. This review systematically analyzes the implementation of advanced nanomaterials, including quan...
An Interpretable Model With Probabilistic Integrated Scoring for Mental Health Treatment Prediction: Design Study.
JMIR medical informatics
Mar 26, 2025
BACKGROUND: Machine learning (ML) systems in health care have the potential to enhance decision-making but often fail to address critical issues such as prediction explainability, confidence, and robustness in a context-based and easily interpretable...
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