Depression

A rapid LC-MS/MS method for simultaneous determination of quetiapine and duloxetine in rat plasma and its application to pharmacokinetic interaction study.

Combinations of new antidepressants like duloxetine and second-generation antipsychotics like quetiapine are used in clinical treatment of major depressive disorder, as well as in forensic toxicology scenarios. The drug-drug interaction (DDI) between quetiapine and duloxetine is worthy of attention to avoid unnecessary adverse effects. However, no pharmacokinetic DDI studies of quetiapine and dulo...

Predeployment predictors of psychiatric disorder-symptoms and interpersonal violence during combat deployment.

BACKGROUND: Preventing suicides, mental disorders, and noncombat-related interpersonal violence during deployment are priorities of the US Army. We used predeployment survey and administrative data to develop actuarial models to identify soldiers at high risk of these outcomes during combat deployment.

Exploring the prediction of emotional valence and pharmacologic effect across fMRI studies of antidepressants.

BACKGROUND: Clinically approved antidepressants modulate the brain's emotional valence circuits, suggesting that the response of these circuits could ...

Self-Efficacy, Poststroke Depression, and Rehabilitation Outcomes: Is There a Correlation?

BACKGROUND: The sudden live changes of stroke survivors may lead to negative psychological and behavioral outcomes, including anxiety and depressive m...

Extracting psychiatric stressors for suicide from social media using deep learning.

BACKGROUND: Suicide has been one of the leading causes of deaths in the United States. One major cause of suicide is psychiatric stressors. The detect...

Drug Repositioning for Schizophrenia and Depression/Anxiety Disorders: A Machine Learning Approach Leveraging Expression Data.

Development of new medications is a lengthy and costly process, and drug repositioning might help to shorten the development cycle. We present a machi...

Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level temporal Bayesian network.

Over the past few decades, the rise of multiple chronic conditions has become a major concern for clinicians. However, it is still not known precisely...

Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps.

PURPOSE OF REVIEW: As rates of suicide continue to rise, there is urgent need for innovative approaches to better understand, predict, and care for th...

Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study.

Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve majo...

Automated depression analysis using convolutional neural networks from speech.

To help clinicians to efficiently diagnose the severity of a person's depression, the affective computing community and the artificial intelligence fi...

Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing.

BACKGROUND: We examined the comparative performance of structured, diagnostic codes vs. natural language processing (NLP) of unstructured text for scr...

Factors associated with dementia in elderly.

We analyzed the factors associated with dementia in the elderly attended at a memory outpatient clinic of the University of Southern Santa Catarina (U...

Survey of potential receptivity to robotic-assisted exercise coaching in a diverse sample of smokers and nonsmokers.

A prior project found that an intensive (12 weeks, thrice weekly sessions) in-person, supervised, exercise coaching intervention was effective for smo...

Identifying Suicide Ideation and Suicidal Attempts in a Psychiatric Clinical Research Database using Natural Language Processing.

Research into suicide prevention has been hampered by methodological limitations such as low sample size and recall bias. Recently, Natural Language P...

Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning.

BACKGROUND: Adolescents have high rates of nonfatal suicide attempts, but clinically practical risk prediction remains a challenge. Screening can be t...

A morphometric signature of depressive symptoms in unmedicated patients with mood disorders.

OBJECTIVE: A growing literature indicates that unipolar depression and bipolar depression are associated with alterations in grey matter volume. Howev...

Automated EEG-based screening of depression using deep convolutional neural network.

In recent years, advanced neurocomputing and machine learning techniques have been used for Electroencephalogram (EEG)-based diagnosis of various neur...

Detection of Suicidality in Adolescents with Autism Spectrum Disorders: Developing a Natural Language Processing Approach for Use in Electronic Health Records.

Over 15% of young people with autism spectrum disorders (ASD) will contemplate or attempt suicide during adolescence. Yet, there is limited evidence c...

Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder.

Electroconvulsive therapy (ECT) is one of the most effective treatments for major depression disorder (MDD). ECT can induce neurogenesis and synaptoge...

Reflecting on the Germanwings Disaster: A Systematic Review of Depression and Suicide in Commercial Airline Pilots.

BACKGROUND: The 2015 Germanwings Flight 9525 disaster, in which 150 people were killed after the co-pilot may have intentionally crashed the plane in ...