Depression

Digital and AI-enhanced psychosocial interventions in breast cancer patients: a systematic review and meta-analysis.

PURPOSE: Digital and AI-supported psychosocial interventions are increasingly implemented to address psychological distress among women with breast cancer. However, their effectiveness and sustainability remain uncertain. This review aimed to systematically evaluate randomised controlled trials (RCTs) assessing the impact of digital and AI-enhanced psychosocial interventions on stress, anxiety, de...

Predicting Momentary Suicidal Ideation From Smartphone Screenshots Using Vision-Language Models: Prospective Machine Learning Study.

BACKGROUND: Passive smartphone sensing shows promise for suicide prevention, but behavioral metadata (GPS, screen time, and accelerometry) often lacks the contextual information needed to detect acute psychological distress. Analyzing what people actually see, read, and type on their phones-rather than just usage patterns-may provide more proximal signals of risk. OBJECTIVE: This study aimed to te...

Impact of a personalized antidepressant prescription using genetics, socio-demographic and clinical data in major depressive disorder patients: A clinical study.

BACKGROUND: Major depressive disorder (MDD) is common and disabling, and antidepressant selection often follows a trial-and-error process. Predictix i...

Multimodal fusion of speech and PHQ-9 for machine learning-based adolescent depression screening.

BACKGROUND: Traditional screening for adolescent depression primarily relies on self-reported symptoms. However, this method is vulnerable to under-re...

A Gamified Pain Management Intervention for Adults With Chronic Pain in Mainland China: Single-Arm Pre-Post Pilot Study With Machine Learning Predictive Modeling.

BACKGROUND: The widespread prevalence of chronic pain (CP) significantly impacts daily functioning worldwide. In mainland China, maintaining engagemen...

Deep learning-based differential diagnosis of major depression and bipolar disorder using microglia-cellular sensors and patient-derived small extracellular vesicles.

It Is the Journey, Not the Destination: Moving From End Points to Trajectories When Assessing Chatbot Mental Health Safety.

Large language models are rapidly becoming embedded in everyday life through artificial intelligence (AI) chatbots that people use for practical assis...

New directions for the Edinburgh Postnatal Depression Scale: towards an EPDS-R (revised).

The Edinburgh Postnatal Depression Scale (EPDS) has been translated into over 60 languages, and for 40 years this 10-item EPDS has remained unchanged....

Identifying minimal risk factors for adolescent suicidal ideation and suicide attempts: A machine learning-optimized approach.

This study aimed to develop and validate a machine learning (ML) model to identify the minimal risk factors for adolescent suicidal behaviors, includi...

Predictive neuroimaging biomarkers of major depressive disorder treatment response: An umbrella review.

Major depressive disorder (MDD) is a heterogeneous condition with varied responses to pharmacological, psychotherapeutic, and neuromodulation interven...

Machine learning-based risk classification of depressive symptoms among patients with hearing loss: evidence from the Health and Retirement Study (HRS).

BACKGROUND: Depression is a common and debilitating condition among individuals with hearing loss, substantially impairing patients' quality of life. ...

LADNET: An MRI-based deep learning approach for Alzheimer's disease detection.

Alzheimer's disease (AD) is considered a leading form of dementia, found in the majority of individuals, and it accounts for the highest prevalence of...

Multimorbidity and major adverse cardiovascular events in antipsychotic users: Time-to-event prediction by explainable machine learning.

Antipsychotic treatment is associated with higher risk of major adverse cardiovascular events (MACEs), and risk may vary by multimorbidity and concomi...

Bipolar disorder relapse detection and prediction using smartwatches. A pilot study for machine learning models using anomaly detection methods.

BACKGROUND: Bipolar disorder (BD) is a chronic mental illness with recurrent mood episodes, with up to 70% of patients relapsing within two years. Ear...

Predicting Ketamine Exposure in Pediatric Status Epilepticus: A Pharmacokinetic-Machine Learning Approach.

BACKGROUND AND OBJECTIVE: Status epilepticus is a life-threatening neurological emergency. Ketamine combined with levetiracetam is a promising therapy...

The impact of the radical urbanization index on depression risk among middle-aged and elderly Chinese: Evidence from a large-scale cross-sectional study.

BACKGROUND: Rapid urbanization in China, while boosting socioeconomic development, has triggered "radical urbanization" characterized by land expansio...

Using natural language processing to assess MDD psychotherapy patterns among Veterans Affairs patients with suicide risk.

Major depressive disorder (MDD) is a leading risk factor for suicide. Within the US Department of Veterans Affairs (VA), psychotherapy is widely used ...

Deciphering risk factors for severe postherpetic neuralgia in patients with herpes zoster: an interpretable machine learning approach.

INTRODUCTION: Postherpetic neuralgia (PHN) is a common complication of herpes zoster (HZ). This study aimed to use a large real-world electronic medic...

AI Chatbots for Mental Health Self-Management: Lived Experience-Centered Qualitative Study.

BACKGROUND: Large language models (LLMs) now enable chatbots to engage in sensitive mental health conversations, including depression self-management....

The prediction and impact of self-criticism on non-suicidal self-injury in adolescents: A study based on machine learning.

This study uniquely combines machine learning with structural equation modeling to investigate how self-criticism affects non-suicidal self-injury (NS...