Depression

QSPR modeling to predict surface tension of psychoanaleptic drugs using the hybrid DA-SVR algorithm.

A robust Quantitative Structure-Property Relationship (QSPR) model was presented to predict the surface tension property of psychoanaleptic (psychostimulant and antidepressant) drugs. A dataset of 112 molecules was utilized, and three feature selection methods were applied: genetic algorithm combined with Ordinary Least Squares (GA-OLS), Partial Least Squares (GA-PLS), and Support Vector Machines ...

Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.

The global prevalence of Major Depressive Disorder (MDD) is increasing at an alarming rate, underscoring the urgent need for timely and accurate diagnoses to facilitate effective interventions and treatments. Electroencephalography remains a widely used neuroimaging technique in psychiatry, due to its non-invasive nature and cost-effectiveness. With the rise of computational psychiatry, the integr...

[Can artificial intelligence help prevent suicide in healthcare workers?].

Federated learning-based prediction of depression among adolescents across multiple districts in China.

Depression in adolescents is a serious mental health condition that can affect their emotional and social well-being. Detailed understanding of depres...

Federated learning and deep learning framework for MRI image and speech signal-based multi-modal depression detection.

Adolescence is a significant period for developing skills and knowledge and learning about managing relationships and emotions by gathering attributes...

Predicting remission after acute phase pharmacotherapy in patients with bipolar I depression: A machine learning approach with cross-trial and cross-drug replication.

OBJECTIVES: Interpatient variability in bipolar I depression (BP-D) symptoms challenges the ability to predict pharmacotherapeutic outcomes. A machine...

Peripheral Blood Mononuclear Cell Biomarkers for Major Depressive Disorder: A Transcriptomic Approach.

Major depressive disorder (MDD) is a complex condition characterized by persistent depressed mood, loss of interest or pleasure, loss of energy or fa...

Exploring key factors influencing depressive symptoms among middle-aged and elderly adult population: A machine learning-based method.

OBJECTIVE: This paper aims to investigate the key factors, including demographics, socioeconomics, physical well-being, lifestyle, daily activities an...

Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency.

BACKGROUND & OBJECTIVES: Mental health disorders pose an increasing public health challenge worsened by the COVID-19 pandemic. The pandemic highlighte...

Identifying the most critical side effects of antidepressant drugs: a new model proposal with quantum spherical fuzzy M-SWARA and DEMATEL techniques.

Identifying and managing the most critical side effects encourages patients to take medications regularly and adhere to the course of treatment. There...

The Most Effective Interventions for Classification Model Development to Predict Chat Outcomes Based on the Conversation Content in Online Suicide Prevention Chats: Machine Learning Approach.

BACKGROUND: For the provision of optimal care in a suicide prevention helpline, it is important to know what contributes to positive or negative effec...

MRI-based deep learning for differentiating between bipolar and major depressive disorders.

Mood disorders, particularly bipolar disorder (BD) and major depressive disorder (MDD), manifest changes in brain structure that can be detected using...

A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly.

BACKGROUND: With the continuous advancement of age in China, attention should be paid to the mental well-being of the elderly population. The present ...

Using Natural Language Processing to develop risk-tier specific suicide prediction models for Veterans Affairs patients.

Suicide is a leading cause of death. Suicide rates are particularly elevated among Department of Veterans Affairs (VA) patients. While VA has made imp...

Future of service member monitoring: the intersection of biology, wearables and artificial intelligence.

While substantial investment has been made in the early identification of mental and behavioural health disorders in service members, rates of depress...

Application Value of a Machine Learning Model in Predicting Mild Depression Associated with Migraine without Aura.

To investigate the application value of a machine learning model in predicting mild depression associated with migraine without aura (MwoA). 178 pat...

Using deep learning and pretreatment EEG to predict response to sertraline, bupropion, and placebo.

OBJECTIVE: Predicting an individual's response to antidepressant medication remains one of the most challenging tasks in the treatment of major depres...

Enhanced network synchronization connectivity following transcranial direct current stimulation (tDCS) in bipolar depression: Effects on EEG oscillations and deep learning-based predictors of clinical remission.

AIM: To investigate oscillatory networks in bipolar depression, effects of a home-based tDCS treatment protocol, and potential predictors of clinical ...

Machine learning-based discrimination of unipolar depression and bipolar disorder with streamlined shortlist in adolescents of different ages.

BACKGROUND: Variations in symptoms and indistinguishable depression episodes of unipolar depression (UD) and bipolar disorder (BD) make the discrimina...

Identification of mitophagy-related genes and analysis of immune infiltration in the astrocytes based on machine learning in the pathogenesis of major depressive disorder.

BACKGROUNDS: Major depressive disorder (MDD) is a pervasive mental and mood disorder with complicated and heterogeneous etiology. Mitophagy, a selecti...