Psychiatry

Characterising symptom clusters: examining profiles of somatic symptoms and their psychosocial predictors among chinese youths using longitudinal data with a machine learning approach.

OBJECTIVE: Somatic symptoms (SS) pose public health burdens, but it remains unclear whether SS should be defined by overall severity or specific symptoms. Therefore, we aimed to identify SS profiles and explore how psychosocial predictors shape these profiles based on the biopsychosocial model. METHODS AND MEASURES: From October to December 2020, Chinese college students (n = 598) completed three ...

Addressing Health Disparities through Community Engagement in Artificial Intelligence-Driven Prevention Science.

Artificial intelligence and machine learning (AI/ML) in prevention science may improve or perpetuate health inequities. Community engagement is one proposed strategy thought to empirically mitigate bias in AI/ML tools. We outline how to incorporate community engagement at every stage of the model development and implementation. Borrowing from a framework for phases of prevention research, we descr...

Artificial Intelligence and the Potential Transformation of Mental Health.

IMPORTANCE: The potential of tools using artificial intelligence (AI) to address the many challenges in delivery of mental health care has been widely...

Developing a Suicide Risk Prediction Algorithm Using Electronic Health Record Data in Mental Health Care: Real-World Case Study.

BACKGROUND: Artificial intelligence (AI) offers potential solutions to address the challenges faced by a strained mental health care system, such as i...

Individuation of predictive parameters of posterior cranial fossa decompression in pediatric patients with Chiari I malformation and autism spectrum disorder.

PURPOSE: There are no specific guidelines for posterior cranial fossa decompression (PCFD) in asymptomatic Chiari Malformation Type I (CM-I) patients ...

A multimodal spatiotemporal convolutional network with attention mechanism for athlete anxiety behavior recognition.

Athletic performance is significantly impacted by anxiety, yet traditional assessment methods rely on subjective questionnaires that lack real-time ca...

Adaptation of the Japanese Version of the 12-Item Attitudes Towards Artificial Intelligence Scale for Medical Trainees: Multicenter Development and Validation Study.

BACKGROUND: In the current era of artificial intelligence (AI), use of AI has increased in both clinical practice and medical education. Nevertheless,...

Bridging online behavior and clinical insight: A longitudinal LLM-based study of suicidality on youtube reveals novel digital markers.

Suicide remains a leading cause of death in Western countries. As social media becomes central to daily life, digital footprints offer valuable insigh...

Randomized evidence on AI-enabled personalized treatment planning for adults with depressive-anxiety Spectrum disorders: A systematic review and Meta-analysis.

BACKGROUND: AI-enabled personalized treatment planning may improve outcomes by tailoring care, yet its clinical impact across modalities remains uncer...

Current treatment options for perinatal depression.

PURPOSE OF REVIEW: Perinatal depression (PND) affects up to one in five patients and is the leading cause of maternal mortality, yet remains underdiag...

Recent advances in the neurogenomics of autism spectrum disorder.

Neurogenomics has provided exceptional insights into the genetic architecture underlying autism spectrum disorder (ASD), which is increasingly underst...

Quality of Life and Decision Regret Risk in Graves' Disease After Radioactive Iodine Therapy: A Prospective Cohort Study.

OBJECTIVE: To identify pre-treatment determinants of hypothyroidism and decision regret (DR) following radioiodine (RAI) therapy in Graves' disease (G...

Advancing precision psychiatry: Machine learning integration with neuroimaging for early detection and diagnosis of Obsessive-Compulsive Disorder.

Early detection of Obsessive-Compulsive Disorder (OCD), a chronic mental health condition characterized by intrusive thoughts and repetitive behaviors...

Fairness-aware K-means clustering in digital mental health for higher education students: a generalizable framework for equitable clustering.

OBJECTIVES: Higher education students, particularly those from underrepresented backgrounds, experience heightened levels of anxiety, depression, and ...

Rumination is more important than other variables in the association between social media use and depression: A multi-method study.

The relationship between social media use (SMU) and mental health, particularly depression, remains inconclusive. The present study recruited 925 coll...

Neural signatures of major depressive disorder with comorbid insomnia: Low gamma PoCG.R-INS.L connectivity as a potential biomarker.

BACKGROUND: Insomnia is a common and distressing symptom in major depressive disorder (MDD). However, research focusing on the neurobiological mechani...

P300-based support vector machine model for identifying adolescent with first-episode, drug-naïve major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is diagnosed mainly through clinical interviews, highlighting a need for objective neurophysiological meas...

Identification of major depressive disorder based on Triple-GCN model constructed with multimodal elastic network from higher-order brain connectivity.

Major Depressive Disorder (MDD) is the common mental health disease threatening human well-being. Several neuroimaging studies show that analyzing neu...

An old disease, a new linguistic challenge for large language models: patient education on psoriasis and psoriatic arthritis in an underrepresented medical language.

OBJECTIVE: Large Language Models (LLMs) are increasingly applied to patient education, yet their performance in languages that are relatively underrep...

Empowering caregivers of individuals with autism spectrum disorder through sensor-based monitoring of emotional dysregulation: A scoping review.

OBJECTIVE: This paper critically reviews existing work in sensor-based emotional dysregulation monitoring to support caregivers of individuals diagnos...