Depression

A Longitudinal Prediction of Suicide Attempts in Borderline Personality Disorder: A Machine Learning Study.

Borderline personality disorder (BPD) is associated with a high risk of suicide. Despite several risk factors being known, identifying vulnerable patients in clinical practice remains a challenge so far. The current study aimed at predicting suicide attempts among BPD patients during disorder-specific psychotherapeutic interventions exploiting machine learning techniques. The study took into accou...

Analysis of User-Generated Posts on Social Media of Adjuvant Analgesics: A Machine Learning Study.

Antiepileptics and antidepressants are frequently prescribed for chronic pain, but their efficacy and potential adverse effects raise concerns, including dependency issues. Increased prescriptions, sometimes fraudulent, prompted reclassification of antiepileptics in some countries. Our aim is to comprehend opinions, perceptions, beliefs, and attitudes towards co-analgesics from online discussions...

Utilising AI technique to identify depression risk among doctoral students.

The phenomenon that the depression risk among doctoral students is higher than that of other groups should not be ignored. Despite this, studies speci...

Evaluating of BERT-based and Large Language Mod for Suicide Detection, Prevention, and Risk Assessment: A Systematic Review.

Suicide constitutes a public health issue of major concern. Ongoing progress in the field of artificial intelligence, particularly in the domain of la...

Estimating cardiovascular mortality in patients with hypertension using machine learning: The role of depression classification based on lifestyle and physical activity.

PURPOSE: This study aims to harness machine learning techniques, particularly the Random Survival Forest (RSF) model, to assess the impact of depressi...

Diagnosis of major depressive disorder using a novel interpretable GCN model based on resting state fMRI.

The diagnosis and analysis of major depressive disorder (MDD) faces some intractable challenges such as dataset limitations and clinical variability. ...

Development of a short form of the Geriatric Depression Scale-30 based on item response theory and the RiskSLIM algorithm.

Recently, methods of quickly and accurately screening for geriatric depression have attracted substantial attention. Short forms of the 30-item Geriat...

Momentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach.

BACKGROUND: Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as ...

How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students? A systematic review.

Undergraduate students are often impacted by depression, anxiety, and stress. In this context, machine learning may support mental health assessment. ...

Comparison between clinician and machine learning prediction in a randomized controlled trial for nonsuicidal self-injury.

BACKGROUND: Nonsuicidal self-injury is a common health problem in adolescents and associated with future suicidal behavior. Predicting who will benefi...

Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study.

OBJECTIVE: Depression has emerged as a global public health concern with high incidence and disability rates, which are timely imperative to identify ...

Real-time assistance in suicide prevention helplines using a deep learning-based recommender system: A randomized controlled trial.

OBJECTIVE: To evaluate the effectiveness and usability of an AI-assisted tool in providing real-time assistance to counselors during suicide preventio...

Screening for Depression and Anxiety Using a Nonverbal Working Memory Task in a Sample of Older Brazilians: Observational Study of Preliminary Artificial Intelligence Model Transferability.

BACKGROUND: Anxiety and depression represent prevalent yet frequently undetected mental health concerns within the older population. The challenge of ...

Model-informed approach to estimate treatment effect in placebo-controlled clinical trials using an artificial intelligence-based propensity weighting methodology to account for non-specific responses to treatment.

In randomized, placebo controlled clinical trials (RCT) in major depressive disorders (MDD), treatment response (TR) is estimated by the change from b...

Predicting drug craving among ketamine-dependent users through machine learning based on brain structural measures.

BACKGROUND: Craving is a core factor driving drug-seeking and -taking, representing a significant risk factor for relapse. This study aims to identify...

Machine Learning-Based Suicide Risk Prediction Model for Suicidal Trajectory on Social Media Following Suicidal Mentions: Independent Algorithm Validation.

BACKGROUND: Previous efforts to apply machine learning-based natural language processing to longitudinally collected social media data have shown prom...

Enhancing suicidal behavior detection in EHRs: A multi-label NLP framework with transformer models and semantic retrieval-based annotation.

BACKGROUND: Suicide is a leading cause of death worldwide, making early identification of suicidal behaviors crucial for clinicians. Current Natural L...

Using machine learning to predict the probability of incident 2-year depression in older adults with chronic diseases: a retrospective cohort study.

BACKGROUND: Older adults with chronic diseases are at higher risk of depressive symptoms than those without. For the onset of depressive symptoms, the...

STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data.

: Early diagnosis of depression is crucial for effective treatment and suicide prevention. Traditional methods rely on self-report questionnaires and ...

Exploring new scientific innovations in combating suicide: a stress detection wristband.

There is a silent pandemic of suicides around the world, with an exponential increase in suicidality and chronic suicidal ideations. The exact global ...