Depression

Psychiatry and Fitness to Fly After Germanwings.

In March 2015, a co-pilot flying Germanwings Flight 9525 deliberately pointed his airplane into a descent, killing himself, five other crew members, and 144 passengers. Subsequent investigation and review teams examined the incident and considered potential lessons to maximize air safety. In this article, aviation industry clinical leaders, including the U.S. Federal Air Surgeon and Chief Psychiat...

Implementing machine learning in bipolar diagnosis in China.

Bipolar disorder (BPD) is often confused with major depression, and current diagnostic questionnaires are subjective and time intensive. The aim of this study was to develop a new Bipolar Diagnosis Checklist in Chinese (BDCC) by using machine learning to shorten the Affective Disorder Evaluation scale (ADE) based on an analysis of registered Chinese multisite cohort data. In order to evaluate the ...

Prospective prediction of suicide attempts in community adolescents and young adults, using regression methods and machine learning.

BACKGROUND: The use of machine learning (ML) algorithms to study suicidality has recently been recommended. Our aim was to explore whether ML approach...

Predicting rehospitalization within 2 years of initial patient admission for a major depressive episode: a multimodal machine learning approach.

Machine learning methods show promise to translate univariate biomarker findings into clinically useful multivariate decision support systems. At curr...

Characterization and Identification of Lysine Succinylation Sites based on Deep Learning Method.

Succinylation is a type of protein post-translational modification (PTM), which can play important roles in a variety of cellular processes. Due to an...

ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder.

Treating patients with major depressive disorder is challenging because it takes several months for antidepressants prescribed for the patients to tak...

A network perspective on body dysmorphic disorder and major depressive disorder.

BACKGROUND: Body dysmorphic disorder (BDD) is a highly debilitating mental disorder associated with notable psychosocial impairment and high rates of ...

Impact of age at onset on the phenomenology of depression in treatment-seeking adults in the STAR*D trial.

BACKGROUND: - Adolescence is characterized by biological, emotional, and behavioral changes. The onset of depression during this vulnerable time may c...

Machine Learning Identifies Large-Scale Reward-Related Activity Modulated by Dopaminergic Enhancement in Major Depression.

BACKGROUND: Theoretical models have emphasized systems-level abnormalities in major depressive disorder (MDD). For unbiased yet rigorous evaluations o...

Physicochemical stability of an admixture of lidocaine and ketamine in polypropylene syringe used in opioid-free anaesthesia.

OBJECTIVES: Opioid-free anaesthesia is a treatment strategy of pain management based on the use of drugs such as lidocaine, ketamine and dexmedetomidi...

cytogenotoxic evaluation of sertraline.

Sertraline (SRT) is an antidepressant agent used as a neuronal selective serotonin-reuptake inhibitor (SSRI). SRT blocks serotonin reuptake and increa...

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone.

BACKGROUND: Although geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood o...

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis.

Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no ro...

A deep learning framework for automatic diagnosis of unipolar depression.

BACKGROUND AND PURPOSE: In recent years, the development of machine learning (ML) frameworks for automatic diagnosis of unipolar depression has escala...

Individualized prediction of depressive disorder in the elderly: A multitask deep learning approach.

INTRODUCTION: Depressive disorder is one of the major public health problems among the elderly. An effective depression risk prediction model can prov...

Machine learning discovery of longitudinal patterns of depression and suicidal ideation.

BACKGROUND AND AIM: Depression is often accompanied by thoughts of self-harm, which are a strong predictor of subsequent suicide attempt and suicide d...

Clustering suicides: A data-driven, exploratory machine learning approach.

Methods of suicide have received considerable attention in suicide research. The common approach to differentiate methods of suicide is the classifica...

Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol.

BACKGROUND: The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relev...

Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features.

BACKGROUND: The development of optimal classification criteria for specific mental disorders which share similar symptoms is an important issue for pr...

Symptomatology differences of major depression in psychiatric versus general hospitals: A machine learning approach.

BACKGROUND: Symptomatology differences of major depressive disorder (MDD) in psychiatric and general hospitals in China leads to possible misdiagnosis...