Depression

Emotional stimulated speech-based assisted early diagnosis of depressive disorders using personality-enhanced deep learning.

BACKGROUND: Early diagnosis of depression is crucial, and speech-based early diagnosis of depression is promising, but insufficient data and lack of theoretical support make it difficult to be applied. Therefore, it is valuable to combine psychiatric theories, collect speech recognition data for depression, and develop a practicable recognition method for depression.

M₂DC: A Meta-Learning Framework for Generalizable Diagnostic Classification of Major Depressive Disorder.

Psychiatric diseases are bringing heavy burdens for both individual health and social stability. The accurate and timely diagnosis of the diseases is essential for effective treatment and intervention. Thanks to the rapid development of brain imaging technology and machine learning algorithms, diagnostic classification of psychiatric diseases can be achieved based on brain images. However, due to ...

A machine learning model using clinical notes to estimate PHQ-9 symptom severity scores in depressed patients.

BACKGROUND: Lack of widespread use of the Patient Health Questionnaire 9-item (PHQ-9) in clinical practice inhibits measurement of treatment follow-up...

AFMDD: Analyzing Functional Connectivity Feature of Major Depressive Disorder by Graph Neural Network-Based Model.

The extraction of biomarkers from functional connectivity (FC) in the brain is of great significance for the diagnosis of mental disorders. In recent ...

Conventional and machine learning-based analysis of age, body weight and body height significance in knot position-related thyrohyoid and cervical spine fractures in suicidal hangings.

The thyrohyoid complex and cervical spine fracture distribution patterns may reflect the knot position as the force distribution by the noose to diffe...

Using natural language processing to identify patterns associated with depression, anxiety, and stress symptoms during the COVID-19 pandemic.

BACKGROUND: Combining data-driven natural language processing techniques with traditional methods using predefined word lists may offer greater insigh...

Machine Learning Tool for New Selective Serotonin and Serotonin-Norepinephrine Reuptake Inhibitors.

Depression, a serious mood disorder, affects about 5% of the population. Currently, there are two groups of antidepressants that are the first-line tr...

Interventions to improve medication adherence in persons with mental disorders.

PURPOSE OF REVIEW: Nonadherence to medication is prevalent in patients with mental illness. Various factors responsible for it. As a result, there are...

Improving explainability of post-separation suicide attempt prediction models for transitioning service members: insights from the Army Study to Assess Risk and Resilience in Servicemembers - Longitudinal Study.

Risk of U.S. Army soldier suicide-related behaviors increases substantially after separation from service. As universal prevention programs have been ...

An Explainable Artificial Intelligence Text Classifier for Suicidality Prediction in Youth Crisis Text Line Users: Development and Validation Study.

BACKGROUND: Suicide represents a critical public health concern, and machine learning (ML) models offer the potential for identifying at-risk individu...

Evaluation of an AI-Based Voice Biomarker Tool to Detect Signals Consistent With Moderate to Severe Depression.

PURPOSE: Mental health screening is recommended by the US Preventive Services Task Force for all patients in areas where treatment options are availab...

Explainable machine learning model for assessing health status in patients with comorbid coronary heart disease and depression: Development and validation study.

BACKGROUND: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health s...

Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review.

BACKGROUND: Prevention of suicide is a global health priority. Approximately 800,000 individuals die by suicide yearly, and for every suicide death, t...

Deep learning based prediction of depression and anxiety in patients with type 2 diabetes mellitus using regional electronic health records.

BACKGROUND: Depression and anxiety are prevalent mental health conditions among individuals with type 2 diabetes mellitus (T2DM), who exhibit unique v...

Identification of depressive symptoms in adolescents using machine learning combining childhood and adolescence features.

BACKGROUND: Depressive symptoms in adolescents can significantly affect their daily lives and pose risks to their future development. These symptoms m...

Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis.

: Recent research has focused on exploring the relationships between various factors associated with headaches and understanding their impact on indiv...

Development and application of a machine learning-based antenatal depression prediction model.

BACKGROUND: Antenatal depression (AND), occurring during pregnancy, is associated with severe outcomes. However, there is a lack of objective and univ...

Guardian-BERT: Early detection of self-injury and suicidal signs with language technologies in electronic health reports.

Mental health disorders, including non-suicidal self-injury (NSSI) and suicidal behavior, represent a growing global concern. Early detection of these...

Psychological and Behavioral Insights From Social Media Users: Natural Language Processing-Based Quantitative Study on Mental Well-Being.

BACKGROUND: Depression significantly impacts an individual's thoughts, emotions, behaviors, and moods; this prevalent mental health condition affects ...

A machine learning-based model for predicting the risk of cognitive frailty in elderly patients on maintenance hemodialysis.

Elderly patients undergoing maintenance hemodialysis (MHD) face a heightened risk of cognitive frailty (CF), which significantly compromises quality o...