Depression

SleepDepNet: A Multi-Task Transformer Framework for Assessing Sleep Quality and Depression Risk from Social Media Narratives

The bidirectional relationship between sleep disturbances and depression presents a serious challenge for digital mental health research and intervention. This study introduces SleepDepNet, a transformer-based multi-task learning model designed to assess sleep quality and depressive sentiment simultaneously from user-generated narratives on Reddit. Leveraging a large, custom-labelled dataset drawn...

Predicting and Preventing Suicide at Entry to Mental Health Care: A Community-Engaged, Machine Learning Model Implementation

Suicide rates in the United States have increased steadily over the past twenty years, a trend coinciding with rising use of mental health services across the country. To help patients before a suicide attempt, health systems must be able to screen for suicide risk and take action at a large scale. Recently, powerful machine learning (ML) models have emerged that can accurately predict suicide att...

The dark side of the mood: structural and functional fronto-insular and cerebellar alterations classify major depression

Despite major depressive disorder (MDD) being the leading cause of disability worldwide, the exact characterization of its neural bases and the develo...

The effect of psychotherapy on the multivariate association between insomnia and depressive symptoms in late-life depression

Late-life depression (LLD) is prevalent in older adults and linked to increased disability, mortality, and suicide risk. Insomnia symptoms are conside...

Leveraging Large Language Models for Digital Phenotyping: Detecting Depressive State Changes for Patients with Depressive Episodes

Digital phenotyping, which takes advantage of data continuously gathered from smartphones and wearable devices, offers promising avenues for real-time...

Changes in psychiatric documentation and treatment in primary care with artificial intelligence scribe use

Despite increasingly widespread use of artificial intelligence-driven ambient scribes in medicine, the extent to which they may impact clinician pract...

Key predictors of maternal mild depression and anxiety in low resource settings: A machine learning approach

Maternal mental health (MMH) disorders, particularly depression and anxiety, are major public health concerns in low- and middle-income countries (LMI...

Biomarkers of the Microbiome-Skin-Brain Axis in Stress and Depression: Fingerprinting of Highly Volatile Compounds in Axillary Sweat via Gas Chromatography-Ion Mobility Spectrometry

Difficulty in the diagnosis of high stress and depression has been recognized conventionally depending on the observation of patient symptoms and psyc...

Machine Learning-Enabled EEG Biomarkers Predict Divergent Antidepressant and Placebo Response in a Clinical Trial of Major Depression

Major depressive disorder (MDD) is a heterogeneous neuropsychiatric disorder with highly variable antidepressant outcomes. In randomized controlled tr...

Deep learning-based polygenic scores enhance generalizability of psychiatric disorders prediction

Polygenic scores (PGSs) have emerged as promising tools for predicting complex traits from genetic data, however, their predictive performance for psy...

Leveraging neighborhood-level Information to Improve Model Fairness in Predicting Prenatal Depression

Perinatal depression (PND) affects 10-20% of pregnant women, with significant racial disparities in prevalence, screening, and treatment. Neighborhood...

Predicting the need for electroconvulsive therapy via machine learning trained on electronic health record data

Electroconvulsive therapy (ECT) is an effective treatment of severe manifestations of mental illness. Since delay in initiation of ECT can have detrim...

Enhancing Mental Health Decision-Making with Artificial Intelligence/Machine Learning: A Prescriptive Analytics Approach for Customised Outcomes

Depression is a complex and widespread mental health condition affecting over 280 million people globally, yet access to timely diagnosis and personal...

A Transparent Four-Feature Logistic Model for Depression Screening in Assisted-Living Facilities

Depression in older adults is both common and frequently underdiagnosed, especially in assisted-living communities, where it often co-occurs with mild...

clickBrick Prompt Engineering: Optimizing Large Language Model Performance in Clinical Psychiatry

Prompt engineering has the potential to enhance large language models’ (LLM) ability to solve tasks through improved in-context learning. In clinical ...

Urinary steroid metabolome shows adrenal, gonadal, and neuroactive steroid dysregulation in adolescents with depression

Steroid hormone profiles in affective disorders suggest hypothalamic– pituitary–adrenal (HPA) axis dysregulation and may reveal novel therapeutic targ...

Patterns of Suicidal Stress Disclosure on Social Media: Integrating Computational and Qualitative Approaches

The lack of understanding of how individuals communicate suicidal stress hinders global suicide intervention plans and practices. This study identifie...

Portability of an artificial intelligence model for self-harm detection across hospital settings

Adequate self-harm surveillance is a key part of suicide prevention efforts. Our prior work has demonstrated the efficacy of an artificial intelligenc...

Leveraging Large Language Models and Patient Portal Messages for Early Identification of Depression

Large language model (LLM)-assisted early warning system may help overcome existing barriers to timely depression diagnosis in patients with cardiovas...

AI-Powered Triage of Suicidal Ideation in Adolescents: A Comparative Evaluation of Large Language Models Using Synthetic Clinical Vignettes

To evaluate the performance of leading Large Language Models (LLMs) in classifying suicide risk and generating clinically appropriate action plans for...