AI Medical Compendium Topic

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Mental Health

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Machine learning in mental health: a scoping review of methods and applications.

Psychological medicine
BACKGROUND: This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide.

Molecular psychiatry
Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful ge...

Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting.

Journal of biomedical informatics
OBJECTIVE: Instruments rating risk of harm to self and others are widely used in inpatient forensic psychiatry settings. A potential alternate or supplementary means of risk prediction is from the automated analysis of case notes in Electronic Health...

Improving well-being in patients with major neurodegenerative disorders: differential efficacy of brief social robot-based intervention for 3 neuropsychiatric profiles.

Clinical interventions in aging
BACKGROUND: Behavioral and psychological symptoms of dementia (BPSD) affect patients' daily life and subjective well-being. International recommendations stress nonpharmacological interventions as first-line treatment. While newer psychosocial initia...

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information.

Journal of biomedical informatics
It is believed that anomalous mental states such as stress and anxiety not only cause suffering for the individuals, but also lead to tragedies in some extreme cases. The ability to predict the mental state of an individual at both current and future...

Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM).

European psychiatry : the journal of the Association of European Psychiatrists
BACKGROUND: Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depress...

A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies.

Statistical methods in medical research
BACKGROUND: Recent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies. Reporting of results on favourable datasets and sampling error in the estimat...

Key Predictors of Generativity in Adulthood: A Machine Learning Analysis.

The journals of gerontology. Series B, Psychological sciences and social sciences
OBJECTIVES: This study aimed to explore a broad range of predictors of generativity in older adults. The study included over 60 predictors across multiple domains, including personality, daily functioning, socioeconomic factors, health status, and me...

Virtual reality and artificial intelligence: the future of mental health. A narrative review.

Recenti progressi in medicina
In recent years, the use of artificial intelligence (AI) and virtual reality (VR) in the psychiatric field has been rapidly developing. This narrative review seeks to provide insight into how these technologies may be used in psychiatric disorders. V...