Depression

From PHQ-2 to PHQ-2W: Data-driven identification of depressed mood and fatigue for optimized weighted depression screening.

OBJECTIVE: This study aims to optimize depression screening tools through a data-driven approach, identifying the most predictive core item combination from the PHQ-9 scale to construct a new simplified depression screening tool. METHODS: Using 11 international datasets, we employed RFECV to select the most predictive item combination from the PHQ-9. Logistic regression models were developed and e...

Artificial intelligence in psychiatry: Current and emerging trends, clinical applications, and research gaps explored through a bibliometric analysis.

OBJECTIVE: The aim of this study is to analyze AI-based scientific publications in psychiatry using bibliometric methods to identify prominent themes, research trends, and future opportunities. METHODS: Original articles published between 1980 and 2025 and indexed in the "Psychiatry" category of the Web of Science were screened. The search strategy included keywords such as "artificial intelligenc...

Plasma metabolomics identifies lipid mediators linking depression and cognitive decline in late-life depression.

BACKGROUND: Late-life depression (LLD) features recurrent episodes and frequently co-exists with cognitive impairment, which predicts worse outcomes a...

Machine learning detecting aggression among mood disorder patients visited in psychiatric emergency departments.

BACKGROUND: Aggression, which is highly prevalent in patients with mood disorders, has been proven valuable in detecting the progression from hypomani...

Identifying diagnostic neuroimaging biomarkers for adolescent major depressive disorder.

BACKGROUND: The increasing incidence of adolescent depression represents a serious public health concern. Despite clear diagnostic criteria, the wide ...

Disrupted brain connectivity in postpartum depression: Insights from resting-state fMRI and machine learning.

BACKGROUND: Postpartum depression (PPD) is a common women's psychological health issue. While studies have identified regional functional abnormalitie...

Optimizing depression diagnosis: fNIRS and machine learning differentiate unipolar, bipolar, and healthy states.

BACKGROUND: Differentiating Unipolar Depression (UD) from Bipolar Depression (BD) remains challenging due to overlapping symptoms, which can lead to m...

Biotypes of deeply phenotyped depressed patients reflect signatures of adverse childhood experience and depressive cognitive biases.

BACKGROUND: Negative Cognitive Styles (NCS) are key features of depression contributing to severe clinical outcomes by sustaining negative affect. How...

Korean longitudinal study on digitally optimized mental healthcare: a cohort profile.

The Korean Longitudinal Study on Digitally Optimized Mental Healthcare is an innovative multicenter trial-ready cohort study. It aims to develop a dig...

Combining network pharmacology, machine learning, molecular docking, molecular simulation dynamics and experimental validation to explore the mechanism of Zhenwu decoction in treating major depression through TNF-α pathways.

BACKGROUND: Major depressive disorder (MDD) is a severe psychophysiological condition characterized by cognitive decline, low energy, weight loss, ins...

Neuroinflammation and insulin resistance in major depression and bipolar disorder: Implications for clinical trials evaluating immunometabolic targeted therapies.

Bipolar disorder (BD) and major depressive disorder (MDD) are highly prevalent, disabling psychiatric illnesses marked by substantial heterogeneity an...

Screening for depressive symptoms in primary and secondary school students based on speech features: A one-year longitudinal study from Jiangsu, China.

OBJECTIVE: This study aims to develop and validate an interpretable prediction model for recognizing depression risk in primary and secondary school s...

Predicting perceived likelihood of future suicide attempts in youth with non-suicidal self-injury: A machine learning approach using LightGBM and SHAP.

OBJECTIVES: Non-suicidal self-injury (NSSI) is a strong predictor and a gateway to suicide attempts (SA) among youth. Therefore, understanding how ind...

Functional near-infrared spectroscopy-based computer-aided diagnosis of major depressive disorder using explainable artificial intelligence: Comparison with conventional machine learning.

BACKGROUND AND OBJECTIVE: Only a limited number of explainable artificial intelligence (XAI) models have been developed for the functional near-infrar...

Artificial Intelligence in Mental Health: Promises and Perils.

The artificial intelligence (AI) revolution has already begun. AI scribes are charting. Chatbots are offering limited psychotherapy services. Research...

Integrating AI-driven technologies and facial-semantic features for depression detection: A cross-sectional study.

BACKGROUND: Depression is a major global health concern, still individuals with depressive tendencies remain undetected in outpatient settings due to ...

Disease and state dependent neural markers in adolescents with BD. Understanding the neural bases of mania, depression and remission in a data fusion approach.

BACKGROUND: Pediatric bipolar disorder (PBD) is a severe and disabling condition marked by alternating episodes of mania and depression, intermitted w...

Differential factor effects in comorbid depressive and anxiety symptoms (CDAS): A machine learning approach to individualized mental health promotion.

BACKGROUND: Depression and anxiety comorbid presents greater severity than isolated symptoms. Traditional analyses often fail to capture complex inter...

Digital phenotyping of depression: A multi-modal passive sensing approach to identifying novel behavioral and physiological markers of treatment response.

BACKGROUND: Despite advances in treatment approaches for Major Depressive Disorder (MDD), significant challenges persist in predicting individual trea...

Leveraging low-cost app-based step count data to assess depression and anxiety in university students: A cross-sectional mobile health study.

BACKGROUND: The prevalence of depression and anxiety among college students worldwide is on the rise, significantly impacting their health and quality...