Psychiatry

Depression

Latest AI and machine learning research in depression for healthcare professionals.

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Breaking the silence: leveraging social interaction data to identify high-risk suicide users online using network analysis and machine learning.

Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervas...

Spatiotemporal discoordination of brain spontaneous activity in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a widespread mental health issue, impacting spatial a...

Trajectory on postpartum depression of Chinese women and the risk prediction models: A machine-learning based three-wave follow-up research.

BACKGROUND: Our study delves into postpartum depression (PPD) extending observation up to six months...

Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression.

Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes i...

The metabolic clock of ketamine abuse in rats by a machine learning model.

Ketamine has recently become an anesthetic drug used in human and veterinary clinical medicine for i...

Predicting the trajectory of non-suicidal self-injury among adolescents.

BACKGROUND: Non-suicidal self-injury (NSSI) is common among adolescents receiving inpatient psychiat...

Gender-specific factors of suicidal ideation among high school students in Yunnan province, China: A machine learning approach.

BACKGROUND: Suicidal ideation (SI) assumes a pivotal role in predicting suicidal behaviors. The inci...

Age-stratified predictions of suicide attempts using machine learning in middle and late adolescence.

BACKGROUND: Prevalence of suicidal behaviour increases rapidly in middle to late adolescence. Predic...

Identifying momentary suicidal ideation using machine learning in patients at high-risk for suicide.

BACKGROUND: Strategies to detect the presence of suicidal ideation (SI) or characteristics of ideati...

Multilevel hybrid handcrafted feature extraction based depression recognition method using speech.

BACKGROUND AND PURPOSE: Diagnosis of depression is based on tests performed by psychiatrists and inf...

Enhancing Major Depressive Disorder Diagnosis With Dynamic-Static Fusion Graph Neural Networks.

Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms ...

Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning.

Neuropsychiatric symptoms (NPS) and mood disorders are common in individuals with mild cognitive imp...

Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records.

Machine Learning models trained from real-world data have demonstrated promise in predicting suicide...

Artificial intelligence in the detection and treatment of depressive disorders: a narrative review of literature.

Modern psychiatry aims to adopt precision models and promote personalized treatment within mental he...

fNIRS-Driven Depression Recognition Based on Cross-Modal Data Augmentation.

Early diagnosis and intervention of depression promote complete recovery, with its traditional clini...

Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans.

Measuring suicide risk fluctuation remains difficult, especially for high-suicide risk patients. Our...

Causes of death in individuals with lifetime major depression: a comprehensive machine learning analysis from a community-based autopsy center.

BACKGROUND: Depression can be associated with increased mortality and morbidity, but no studies have...

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