Depression

Beyond Adverse Childhood Experiences: What Should be Considered for Trauma-Focused Adolescent Mental Health Risk Assessments?

To align with emerging policies for adolescents, feasible, accurate, and equitable trauma-focused assessment protocols need to be developed. To date, most research on this topic has focused on whether traditional adverse childhood experiences (i.e., maltreatment, impaired caregiving) can adequately index mental health risk. Yet, there are noted clinical and statistical drawbacks to this approach. ...

Predicting the First Onset of Suicidal Thoughts and Behaviors in Adolescents Using Multimodal Risk Factors: A 4-Year Longitudinal Study.

OBJECTIVE: Suicide is one of the leading causes of death among youth worldwide, yet existing studies that aimed to predict the first onset of suicidal thoughts and behaviors (STB) included a limited number of data modalities and/or focused on adult populations. This study aimed to prospectively predict first-onset STB across 4-year follow-ups in adolescents using an existing STB history classifica...

Predictive Modeling of Comorbid Depression and Anxiety Symptoms Among Prospective University Students: A GIS-Based and Machine Learning Study.

Prospective university students are highly susceptible to mental health issues such as depression and anxiety. This study investigates the prevalence ...

Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech.

Psychosis poses substantial social and healthcare burdens. The analysis of speech is a promising approach for the diagnosis and monitoring of psychosi...

From Snapshots to Stable Outcomes: Resting-State Functional Magnetic Resonance Imaging-Based Prognosis of Functioning in Patients With Psychosis Risk or Recent-Onset Depression.

BACKGROUND: Early recovery of functioning is critical for favorable outcomes in psychotic and affective disorders. Transdiagnostic brain activity patt...

Depression EEG classification based on multi-scale convolutional transformer network.

Depression electroencephalograph (EEG) classification based on machine learning is helpful for the auxiliary diagnosis of major depression disorder (M...

Assessment of suicidal risk factors in young depressed persons with non-suicidal self-injury based on an artificial intelligence.

INTRODUCTION: The role of non-suicidal self-injury (NSSI) in the suicide process of people with major depressive disorder(MDD) remains controversial. ...

Harnessing technology for hope: a systematic review of digital suicide prevention tools.

This systematic review scrutinizes digital interventions in suicide prevention, telehealth, mobile applications, artificial intelligence (AI), and dig...

Comparing traditional natural language processing and large language models for mental health status classification: a multi-model evaluation.

The substantial increase in mental health disorders globally necessitates scalable, accurate tools for detecting and classifying these conditions in d...

Multi-kingdom microbiota analysis reveals bacteria-viral interplay in IBS with depression and anxiety.

Irritable Bowel Syndrome (IBS) is a common gastrointestinal disorder frequently accompanied by psychological symptoms. Bacterial microbiota plays a cr...

Assessing psychological resilience and its influencing factors in the MSM population by machine learning.

This study assesses the influence of social support, self-esteem, depression, and education on psychological resilience among men who have sex with me...

Characteristics of brain network connectome and connectome-based efficacy predictive model in bipolar depression.

Aberrant functional connectivity (FC) between brain networks has been indicated closely associated with bipolar disorder (BD). However, the previous f...

Prediction of suicide using web based voice recordings analyzed by artificial intelligence.

The integration of machine learning (ML) and deep learning models in suicide risk assessment has advanced significantly in recent years. In this study...

Development and validation of a risk prediction model for depression in patients with chronic obstructive pulmonary disease.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a prevalent respiratory condition often accompanied by depression, which exacerbates disea...

Interpretable machine learning for depression recognition with spatiotemporal gait features among older adults: a cross-sectional study in Xiamen, China.

OBJECTIVE: Depression in older adults is a growing public health concern, yet there is still a lack of convenient and real-time methods for depressive...

Crisis-line workers' perspectives on AI in suicide prevention: a qualitative exploration of risk and opportunity.

BACKGROUND: Crisis support services offer crucial intervention for individuals in acute distress, providing timely access to trained volunteers whose ...

Recognition of anxiety and depression using gait data recorded by the kinect sensor: a machine learning approach with data augmentation.

Anxiety and depression disorders are increasingly common, necessitating methods for real-time assessment and early identification. This study investig...

Different prefrontal cortex activity patterns in bipolar and unipolar depression during verbal fluency tasks based on functional near infrared spectroscopy study.

This study aimed to investigate the functionality of the prefrontal cortex in patients with unipolar depression (UD) and bipolar depression (BD) using...

Depression detection methods based on multimodal fusion of voice and text.

Depression is a prevalent mental health disorder, and early detection is crucial for timely intervention. Traditional diagnostics often rely on subjec...

Machine learning-based model for behavioural analysis in rodents applied to the forced swim test.

The Forced Swim Test (FST) is a widely used preclinical model for assessing antidepressant efficacy, studying stress response, and evaluating depressi...