AIMC Topic: Mental Disorders

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Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis.

Neuroscience and biobehavioral reviews Mar 26, 2024
Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising pre...
Machine Learning Mental Disorders Humans Magnetic Resonance Imaging Connectome Prognosis Treatment Outcome
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Artificial intelligence GPT-4: A game changer in the advancement of psychiatric rehabilitation in the new millennium.

Asian journal of psychiatry Feb 21, 2024
Mental Disorders Psychiatric Rehabilitation Humans Artificial Intelligence
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Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review.

Reviews in the neurosciences Feb 5, 2024
Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides ...
Biomarkers Spectroscopy, Near-Infrared Machine Learning Autism Spectrum Disorder Humans Mental Disorders Support Vector Machine
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A deep learning quantification of patient specificity as a predictor of session attendance and treatment response to internet-enabled cognitive behavioural therapy for common mental health disorders.

Journal of affective disorders Jan 19, 2024
BACKGROUND: Increasing an individual's ability to focus on concrete, specific detail, thus reducing the tendency toward overly broad, decontextualised generalisations about the self and world, is a target within cognitive behavioural therapy (CBT). H...
Treatment Outcome Cognitive Behavioral Therapy Deep Learning Humans Mental Disorders Female Mental Health
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Bridging the Skills Gap: Evaluating an AI-Assisted Provider Platform to Support Care Providers with Empathetic Delivery of Protocolized Therapy.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 11, 2024
Despite the high prevalence and burden of mental health conditions, there is a global shortage of mental health providers. Artificial Intelligence (AI) methods have been proposed as a way to address this shortage, by supporting providers with less ex...
Artificial Intelligence Humans Mental Disorders
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Evaluation of deep learning-based depression detection using medical claims data.

Artificial intelligence in medicine Dec 4, 2023
Human accuracy in diagnosing psychiatric disorders is still low. Even though digitizing health care leads to more and more data, the successful adoption of AI-based digital decision support (DDSS) is rare. One reason is that AI algorithms are often n...
Deep Learning Depression Adult Humans Algorithms Mental Disorders
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Arts therapies for mental disorders in COVID-19 patients: a comprehensive review.

Frontiers in public health Dec 4, 2023
BACKGROUND AND OBJECTIVE: The COVID-19 global pandemic has necessitated the urgency for innovative mental health interventions. We performed a comprehensive review of the available literature on the utility and efficacy of arts therapies in treating ...
Mental Health Humans COVID-19 Health Personnel Mental Disorders
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Psychological factors underlying attitudes toward AI tools.

Nature human behaviour Nov 20, 2023
What are the psychological factors driving attitudes toward artificial intelligence (AI) tools, and how can resistance to AI systems be overcome when they are beneficial? Here we first organize the main sources of resistance into five main categories...
Cognition Intention Empirical Research Artificial Intelligence Mental Disorders Humans
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Ten Simple Rules for Using Machine Learning in Mental Health Research.

Biological psychiatry Nov 18, 2023
Biomedical Research Mental Disorders Mental Health Machine Learning Humans
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Risk of data leakage in estimating the diagnostic performance of a deep-learning-based computer-aided system for psychiatric disorders.

Scientific reports Oct 3, 2023
Deep-learning approaches with data augmentation have been widely used when developing neuroimaging-based computer-aided diagnosis (CAD) systems. To prevent the inflated diagnostic performance caused by data leakage, a correct cross-validation (CV) me...
Humans Neural Networks, Computer Diagnosis, Computer-Assisted Deep Learning Computers Mental Disorders
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