Psychiatry

Depression

Latest AI and machine learning research in depression for healthcare professionals.

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Predicting Suicidality in people living with HIV in Uganda: A Machine Learning Approach

People living with HIV (PLWH) are more likely to experience suicidal thoughts and exhibit suicidal b...

Blood Immuno-metabolic Biomarker Signatures of Depression and Affective Symptoms in Young Adults

Depression is associated with alterations in immuno-metabolic biomarkers, but it remains unclear whe...

Supervised Machine-Learning Classification of Treatment-Resistant Depression in U.S. Claims Data

Accurate identification of individuals with treatment-resistant depression (TRD) is important to fac...

Exploring the Potential of Large Language Models for Automated Safety Plan Scoring in Outpatient Mental Health Settings

The Safety Planning Intervention (SPI) produces a plan to help manage patients’ suicide risk. High-q...

Polygenic Risk-Informed White Matter Integrity Improves Deep Learning-Based Prediction of Youth Depression

Early detection of youth depression is crucial, given its rising prevalence and long-term consequenc...

Towards Understanding Bipolar Disorder Through Social Media and Transformer Models: Challenges and Insights

Social media presents a promising avenue for monitoring mental health, yet detecting bipolar disorde...

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 challeng...

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 coinc...

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 c...

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, mortalit...

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 wearab...

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, t...

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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...

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 an...

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 d...

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 ...

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...

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-livi...

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