Psychiatry

Exploring mental health literacy on twitter: A machine learning approach.

OBJECTIVES: This study investigates whether reducing mental illness stigma, enhancing help-seeking e...

Prediction of first attempt of suicide in early adolescence using machine learning.

BACKGROUND: Suicide is the second leading cause of death among early adolescents, yet the first onse...

Correction to: Research on the selfefficacy and resilience of female graduate students in the era of artificial intelligence: analysis of the mechanism of mobile phone dependence, anxiety and mentoring relationship.

CdS/CZTSSe heterojunction synaptic memristor: Enabling efficient handwritten digit recognition.

Owing to the outstanding performance of memristors in brain-like parallel computing and data process...

Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data.

Accurate prediction of depressive symptoms in healthy individuals can enable early intervention and ...

Machine learning-driven risk prediction and feature identification for major depressive disorder and its progression: an exploratory study based on five years of longitudinal data from the US national health survey.

BACKGROUND: Major depressive disorder (MDD) presents significant public health challenges due to its...

Prediction of remission of pharmacologically treated psychotic depression: A machine learning approach.

BACKGROUND: The combination of antidepressant and antipsychotic medication is an effective treatment...

Multimodal depression recognition and analysis: Facial expression and body posture changes via emotional stimuli.

BACKGROUND: Clinical studies have shown that facial expressions and body posture in depressed patien...

Prediction of Adolescent Suicide Attempt by Integrating Clinical, Neurocognitive and Geocoded Neighborhood Environment Data.

BACKGROUND AND HYPOTHESIS: Suicide attempt is a complex behavior influenced by a combination of fact...

Modeling the Determinants of Subjective Well-Being in Schizophrenia.

BACKGROUND: The ultimate goal of successful schizophrenia treatment is not just to alleviate psychot...

Incorporating end-user perspectives into the development of a machine learning algorithm for first time perinatal depression prediction.

OBJECTIVE: Machine learning algorithms can advance clinical care, including identifying mental healt...

Machine learning-based prediction of anxiety disorders using blood metabolite and social trait data from the UK Biobank.

Anxiety disorders are the most prevalent type of mental health disorders and are characterised by ex...

Prenatal exposure to criteria air pollution and traffic-related air toxics and risk of autism spectrum disorder: A population-based cohort study of California births (1990-2018).

BACKGROUND: Autism spectrum disorder (ASD) prevalence has risen steadily in California (CA) over sev...

Artificial intelligence in forensic mental health: A review of applications and implications.

This narrative review explores the transformative role of artificial intelligence (AI) in forensic m...

Brain Fractal Dimension and Machine Learning can predict first-episode psychosis and risk for transition to psychosis.

Although there are notable structural abnormalities in the brain associated with psychotic diseases,...

Artificial intelligence in autism spectrum disorder: A path to early detection and improved outcomes.

Distinct processing stages of cross-modal conflict in schizophrenia: The role of auditory cortex underactivation.

BACKGROUND: The cross-modal conflict deficit is a key feature of schizophrenia. However, it remains ...

Machine learning applications related to suicide in military and Veterans: A scoping literature review.

OBJECTIVE: Suicide remains one of the main preventable causes of death among service members and vet...

Beyond total scores: Enhancing psychotherapy outcome prediction with item-level scores.

OBJECTIVE: This study aims at improving dropout and treatment nonresponse prevention by optimizing t...

Prediction of post stroke depression with machine learning: A national multicenter cohort study.

OBJECTIVE: Post-stroke depression (PSD) is a common psychiatric complication following stroke, with ...

Identifying metabolites of new psychoactive substances using in silico prediction tools.

New psychoactive substances (NPS) pose an increasing challenge for clinical and forensic toxicology ...