Depression

Improving speech depression detection using transfer learning with wav2vec 2.0 in low-resource environments.

Depression, a pervasive global mental disorder, profoundly impacts daily lives. Despite numerous deep learning studies focused on depression detection through speech analysis, the shortage of annotated bulk samples hampers the development of effective models. In response to this challenge, our research introduces a transfer learning approach for detecting depression in speech, aiming to overcome c...

Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model.

OBJECTIVES: COVID-19 epidemics often lead to elevated levels of depression. To accurately identify and predict depression levels in home-quarantined individuals during a COVID-19 epidemic, this study constructed a depression prediction model based on multiple machine learning algorithms and validated its effectiveness.

Using machine learning to develop a five-item short form of the children's depression inventory.

BACKGROUND: Many adolescents experience depression that often goes undetected and untreated. Identifying children and adolescents at a high risk of de...

Elucidating the influence of familial interactions on geriatric depression: A comprehensive nationwide multi-center investigation leveraging machine learning.

OBJECTIVE: A plethora of studies have unequivocally established the profound significance of harmonious familial relationships on the psychological we...

The therapeutic effectiveness of artificial intelligence-based chatbots in alleviation of depressive and anxiety symptoms in short-course treatments: A systematic review and meta-analysis.

BACKGROUND: The emergence of artificial intelligence-based chatbot has revolutionized the field of clinical psychology and psychotherapy, granting ind...

Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning.

BACKGROUND: There is mounting evidence that asthma might exacerbate depression. We sought to examine candidates for diagnostic genes in patients suffe...

The Effect of Social Robots on Depression and Loneliness for Older Residents in Long-Term Care Facilities: A Meta-Analysis of Randomized Controlled Trials.

OBJECTIVES: Depression and loneliness are challenges facing older residents living in long-term care facilities. Social robots might be a solution as ...

Intelligent classification of major depressive disorder using rs-fMRI of the posterior cingulate cortex.

Major Depressive Disorder (MDD) is a widespread psychiatric condition that affects a significant portion of the global population. The classification ...

Predicting post-treatment symptom severity for adults receiving psychological therapy in routine care for generalised anxiety disorder: a machine learning approach.

Approximately half of generalised anxiety disorder (GAD) patients do not recover from first-line treatments, and no validated prediction models exist ...

An Artificial LiSiO Nociceptor with Neural Blockade and Self-Protection Abilities.

An artificial nociceptor, as a critical and special bionic receptor, plays a key role in a bioelectronic device that detects stimuli and provides warn...

Validation of Machine Learning-Based Assessment of Major Depressive Disorder from Paralinguistic Speech Characteristics in Routine Care.

New developments in machine learning-based analysis of speech can be hypothesized to facilitate the long-term monitoring of major depressive disorder ...

Unraveling the distinction between depression and anxiety: A machine learning exploration of causal relationships.

OBJECTIVE: Depression and anxiety, prevalent coexisting mood disorders, pose a clinical challenge in accurate differentiation, hindering effective hea...

Hierarchical Convolutional Attention Network for Depression Detection on Social Media and Its Impact During Pandemic.

People across the globe have felt and are still going through the impact of COVID-19. Some of them share their feelings and suffering online via diffe...

Explainable artificial intelligence models for predicting risk of suicide using health administrative data in Quebec.

Suicide is a complex, multidimensional event, and a significant challenge for prevention globally. Artificial intelligence (AI) and machine learning (...

BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping.

Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are ...

Optimizing precision medicine for second-step depression treatment: a machine learning approach.

BACKGROUND: Less than a third of patients with depression achieve successful remission with standard first-step antidepressant monotherapy. The proces...

Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model.

BACKGROUND: Prior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challe...

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.

Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for ...

Personality traits as predictors of depression across the lifespan.

BACKGROUND: Depression is a major public health concern. A barrier for research has been the heterogeneous nature of depression, complicated by the ca...

Analysis and evaluation of explainable artificial intelligence on suicide risk assessment.

This study explores the effectiveness of Explainable Artificial Intelligence (XAI) for predicting suicide risk from medical tabular data. Given the co...