Psychiatry

Machine Learning Algorithms for Predicting Injurious Fall Risk Among Older Adults With Depression: A Prognostic Modeling Study.

BACKGROUND: Falls and related injuries (FRI) pose a large burden among older adults with depression. Proactively identifying individuals at high FRI risk enables timely and tailored interventions, reducing unnecessary health care resource utilization. However, prior prediction models relied on fixed time intervals and failed to capture dynamic changes in health status over time. OBJECTIVES: To dev...

Deep learning for autism detection using clinical notes: A comparison of transfer learning for a transparent and black-box approach.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition whose rising prevalence places increasing demands on a lengthy diagnostic process. Machine learning (ML) has shown promise in automating ASD diagnosis, but most existing models operate as black boxes and are typically trained on a single dataset, limiting their generalizability. In this study, we introduce a transparent and i...

Trajectories of affective disorders-the central structures of CRC/TRR 393.

The recurrent and often unpredictable course of affective disorders poses a critical challenge for long-term patient care. The CRC/TRR 393 consortium ...

Healthbots for conducting clinical screening and remote monitoring with patient mood assessment: A scoping review.

BACKGROUND: Patient mood assessment is key in managing chronic diseases but is often overlooked. Although conversational agents enhance telemonitoring...

Applications of artificial intelligence-based conversational agents in healthcare: A systematic umbrella review.

BACKGROUND: Artificial intelligence-based conversational agents (AI-based CAs) have emerged as essential tools for communication and service delivery ...

Delineating white matter phenotypes of sensori-/psychomotor functioning in large-scale cohorts of healthy individuals and patients with mental disorders across the lifespan (whiteSPAN): rationale and methods of an interdisciplinary bicentric project.

Aberrant sensori-/psychomotor functioning-including muscular hand weakness, sedentary behavior, psychomotor agitation, slowing, agitation, apathy, and...

Thank You for Choosing Public Health Education: A Reminder to Students Why We Need You.

This commentary captures the keynote addressed shared by the 2024 SOPHE President during the organization's 75th anniversary meeting in Long Beach, Ca...

Linking speech patterns to brain structure in affective and psychotic disorders: an integrative natural language processing approach.

Language disturbances are central features of serious mental illnesses, yet traditional clinical assessments often rely on subjective evaluation that ...

Trajectories of affective disorders: neurobiological mechanisms during symptom change.

Effective treatment of affective disorders (AD) requires a deep understanding of the underlying neurobiological mechanisms. However, in machine-learni...

Machine learning identifies traumatic experiences as being associated with auditory verbal hallucinations in both a non-clinical population and individuals diagnosed with psychosis.

BACKGROUND: Auditory verbal hallucinations (AVHs) are a core symptom of psychosis but their prevalence in the general population ranges from 5-28 %. M...

Detecting psychosis via natural language processing of social media posts: potentials and pitfalls.

The early detection and continuous monitoring of psychosis is of utmost importance in ensuring timely and effective treatment. Current mental health c...

Towards Human-Centered Digital Health Interventions.

This article discusses the potential of digital health interventions (DHIs) in mental health care, emphasizing the importance of designing them to fos...

Bridging tradition and innovation: Acupuncture for depression through clinical efficacy and neurobiological insights.

BACKGROUND: To elucidate the research progress on acupuncture therapy as a complementary and alternative medicine treatment for depression by reviewin...

Early identification of pediatric depression in western China: A multicenter, citywide evaluation of nine machine learning models.

BACKGROUND: Childhood depression is an emerging global concern, yet knowledge and early detection tools remain limited in low- and middle-income regio...

Identification of the core genes KLRB1 and RETN as potential shared diagnostic markers for major depressive disorder and systemic lupus erythematosus through bioinformatics and machine learning methodologies.

This study investigates the shared molecular mechanisms between major depressive disorder (MDD) and systemic lupus erythematosus (SLE) through integra...

Machine learning-assisted multicolor identification and quantification of antidepressant drugs by waste-derived fluorescent nanoprobes: Towards green AI-based electronic tongue.

Recently, the severe side effects related to the widespread consumption of antidepressants (ADs) have alarmingly created a global challenge for clinic...

Electroencephalography-Based Machine and Deep Learning Approaches for the Diagnosis of Dissociative Disorders: A Comprehensive Review.

Dissociative disorders (DDs), including dissociative identity disorder and depersonalization disorder, are complex and often misdiagnosed psychiatric ...

Decoding depression with computer vision-assisted analysis of synchronized facial expressions.

BACKGROUND: Facial expressions convey nonverbal signals of internal emotional states and hold potential as important markers for affective disorders s...

Revisiting drunk driving risk among individuals with alcohol use disorder using unsupervised learning: From clinical characteristics and neuropsychological performance to EEG data.

The strong association between alcohol use disorder (AUD) and driving under the influence of alcohol (DUIA) suggests substantial overlaps across the b...

Effective alternative strategies to combat challenges associated with MDR bacterial infections: Drug repurposing, role of artificial intelligence, and novel therapeutic options.

Antibiotic failure has emerged as a critical global health concern, driven by a combination of factors including the stagnation in the discovery of ne...