Psychiatry

Race/ethnicity, disability, and antenatal depression in the United States: population-level insights from machine learning.

OBJECTIVE: Women with intersecting identities, such as being both Black and disabled, face heightened risk of antenatal depression, yet few studies examine its nuanced mechanisms. To capture complex, interactive associations among risk factors, we applied explainable machine learning to predict antenatal depression and identify key predictors among non-Hispanic Black (NHB) and non-Hispanic White (...

Association between the oxidative balance score and depressive symptoms in adults with metabolic syndrome: a cross-sectional study from NHANES.

OBJECTIVE: This study investigated the association between the Oxidative Balance Score (OBS) and the odds of depressive symptoms in adults with metabolic syndrome (MetS) using National Health and Nutrition Examination Survey data (2007-2018), and employed machine learning to enhance predictive insights. METHODS: We analyzed 6,244 U.S. adults with MetS. Multivariable logistic regression assessed th...

Navigating the complexity of AI adoption in psychotherapy by identifying key facilitators and barriers.

Artificial intelligence (AI) technologies in mental healthcare offer promising opportunities to reduce therapists' burden and enhance healthcare deliv...

Integrative analysis via bioinformatics and machine learning identifies SERPING1 as a biomarker candidate for major depressive disorder.

Beyond acoustic features: Incorporating linguistic variables in automatic speech analysis for depression detection.

BACKGROUND: Most research on automatic speech analysis (ASA) has focused on acoustic features, while the potential of linguistic markers remains under...

The association between psychotropic medications and cognitive functioning in bipolar disorder.

BACKGROUND: Many individuals with bipolar disorder have decreased levels of cognitive functioning even when in a euthymic mood state. Persons with bip...

From pharmacokinetic genes to polygenic scores and multi-omic integration: advances towards precision psychiatry.

INTRODUCTION: . Pharmacological treatment is the mainstay in the acute and long-term management of severe mental disorders such as major depressive di...

AI and Qualitative Health Research: Working Through a Necessary Grieving Process.

Qualitative health research has been shaken by the rapid uptake of artificial intelligence (AI), especially large language models. Drawing on Kübler-R...

Transcriptomic analysis and machine learning have identified shared diagnostic genes and a possible mechanism linking bipolar disorder and epilepsy.

It is well known that bipolar disorder (BD) and epilepsy (EP) are common neurological diseases. The objective of this study was to screen for potentia...

AI-Driven Mental Health Support for Caregivers of Individuals With Alzheimer Disease: Systematic Literature Review and Development of a Conceptual Framework.

BACKGROUND: Caregivers supporting individuals with Alzheimer disease and related dementias (AD/ADRD) frequently encounter prolonged emotional strain, ...

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability.

Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning (ML) systems into high-stakes domains such as digit...

Artificial intelligence-associated delusions and large language models: risks, mechanisms of delusion co-creation, and safeguarding strategies.

Large language models (LLMs) are poised to become a ubiquitous feature of everyday life, mediating communication, decision making, and information cur...

Neuroimaging insights into the neurophysiological subtypes of major depressive disorder.

Major depressive disorder (MDD) is a highly heterogeneous condition that limits the reliability of symptom-based diagnosis and treatment selection. In...

Classification of suicide attempt history in adolescents with affective disorders: An exploratory machine learning study.

INTRODUCTION: Diagnosis of affective disorders among adolescent population links with the high risk of suicide attempt. The use of clinical psychologi...

Evaluating the potential of applying artificial intelligence in the diagnosis of major depressive disorder: Neuroimaging and clinical behavioral markers.

BACKGROUND: Consensus exists that point-of-care in scalable capabilities are required to improve the timeliness and accuracy of Major Depressive Disor...

Using machine learning to reveal two distinct neuroanatomical subtypes of first-episode, drug-naïve major depressive disorder: Evidence from the REST-meta-MDD project.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogeneous condition, complicating biomarker discovery and precision medicine. Identifying ...

Psychosis Risk and Generative Artificial Intelligence Use Frequency, Motivations, and Delusion-Like Experiences: Cross-Sectional Survey Study.

BACKGROUND: Growth of generative artificial intelligence (GenAI) has exploded in recent years. Many have noted its substantial potential to increase a...

A data-centric approach to detecting and mitigating demographic bias in pediatric mental health text.

BACKGROUND: Healthcare Artificial Intelligence (AI) offers transformative potential but often inherits biases from training data, worsening disparitie...

Development and validation of an interpretable multi-task prediction model based on heart rate variability for discriminating depression, anxiety and psychiatric comorbidities: a machine learning-driven retrospective study.

Task complexity and AI dependency among college students: the mediating roles of cognitive load, future anxiety, and task motivation.

PURPOSE: This study aims to clarify the associations between task complexity and Artificial Intelligence (AI) dependency among university students and...