Depression

TTFNet: Temporal-Frequency Features Fusion Network for Speech based Automatic Depression Recognition and Assessment.

Related studies have revealed that the phonological features of depressed patients are different fro...

Inflammatory biomarkers as predictors for unlocking antidepressant efficacy: Assessing predictive value and risk stratification in major depressive disorder in a prospective longitudinal study.

BACKGROUND: Major depressive disorder (MDD) is characterized by significant heterogeneity in treatme...

Contrastive functional connectivity defines neurophysiology-informed symptom dimensions in major depression.

Major depressive disorder (MDD) is highly heterogeneous, posing challenges for effective treatment d...

Predicting rTMS treatment response in depression: use of machine learning models to identify the roles of metabolic and clinical factors.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depres...

Machine Learning and Artificial Intelligence in Suicide Prevention: A Bibliometric Analysis of Emerging Trends and Implications for Nursing.

Nurses play a crucial role in suicide prevention, yet the integration of artificial intelligence and...

Brain stimulation outcome prediction in Major Depressive Disorder by deep learning models using EEG representations.

Major Depressive Disorder (MDD) is known as a widespread illness and needs a timely treatment. The t...

Hemoglobin - to - red cell distribution width ratio in depression symptoms: Threshold effects and metabolic - inflammatory mediation revealed by multimodal machine learning and symptom network analysis in 196,260 adults.

OBJECTIVE: To investigate the association between hemoglobin to red blood cell distribution width ra...

Machine learning models of depression in middle-aged and older adults with cardiovascular metabolic diseases.

BACKGROUND: The incidence of cardiovascular metabolic diseases (CMD) is increasing, and depression i...

Functional connectome-based predictive modeling of suicidal ideation.

Suicide represents an egregious threat to society despite major advancements in medicine, in part du...

Multiband EEG signatures decoded using machine learning for predicting rTMS treatment response in MDD.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for major d...

Depression is associated with treatment response trajectories in adults with Prolonged Grief Disorder: A machine learning analysis.

BACKGROUND: Although evidence-based Prolonged Grief Disorder treatments (PGDT) exist, pretreatment c...

The Role of Generative Artificial Intelligence in Evaluating Adherence to Responsible Press Media Reports on Suicide: A Multi-Site, Three-Language Study.

Predicting treatment response in individuals with major depressive disorder using structural MRI-based similarity features.

BACKGROUND: Major Depressive Disorder (MDD) is a prevalent mental health condition with significant ...

Identifying key risk factors for intentional self-harm, including suicide, among a cohort of people prescribed opioid agonist treatment: A predictive modelling study.

BACKGROUND AND AIMS: People with opioid use disorder are at increased risk of intentional self-harm ...

A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights.

BACKGROUND: Major Depressive Disorder is a leading cause of disability worldwide. An accurate assess...

Optimizing Treatment for Depression in Primary Care Using Psychotherapy Versus Antidepressant Medication in a Low-Resource Setting: Protocol for the OptimizeD Randomized Controlled Trial.

Psychotherapy and antidepressant medications are first-line treatments for depression, and they bot...

[Psilocybin in the setting of treatment-resistant unipolar depression: A case report].

BACKGROUND: Current antidepressants have shown certain limitations in the treatment of unipolar depr...

Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform.

Post-COVID-19, depression rates have risen sharply, increasing the need for early diagnosis using el...

Unlocking the Potential of Wear Time of a Wearable Device to Enhance Postpartum Depression Screening and Detection: Cross-Sectional Study.

BACKGROUND: Postpartum depression (PPD) is a mood disorder affecting 1 in 7 women after childbirth t...

Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review.

BACKGROUND: Differentiating bipolar disorder (BD) from unipolar depression (UD) is essential, as the...

Speech and Voice Quality as Digital Biomarkers in Depression: A Systematic Review.

OBJECTIVE: To review the current evidence on the use of artificial intelligence-driven speech and vo...