Depression

Comparing clinical decision-making of AI technology to a multi-professional care team in an electronic cognitive behavioural therapy program for depression: protocol.

INTRODUCTION: Depression is a leading cause of disability worldwide, affecting up to 300 million people globally. Despite its high prevalence and debilitating effects, only one-third of patients newly diagnosed with depression initiate treatment. Electronic cognitive behavioural therapy (e-CBT) is an effective treatment for depression and is a feasible solution to make mental health care more acce...

Unexpectedly Prolonged Serotonin Syndrome and Fatal Complications Following a Massive Overdose of Paroxetine Controlled-Release.

Symptoms caused by a selective serotonin reuptake inhibitor (SSRI) overdose are often mild and can be managed with supportive care and close monitoring, even when complicated by serotonin syndrome. There are limited pharmacokinetic data regarding massive overdoses of paroxetine, and the severity of an SSRI overdose is likely to be underestimated. We describe a fatal case of severe serotonin syndro...

The steps that young people and suicide prevention professionals think the social media industry and policymakers should take to improve online safety. A nested cross-sectional study within a Delphi consensus approach.

INTRODUCTION: Concerns exist about the relationship between social media and youth self-harm and suicide. Study aims were to examine the extent to whi...

Exploring the role of AI in classifying, analyzing, and generating case reports on assisted suicide cases: feasibility and ethical implications.

This paper presents a study on the use of AI models for the classification of case reports on assisted suicide procedures. The database of the five Du...

Deconstructing depression by machine learning: the POKAL-PSY study.

Unipolar depression is a prevalent and disabling condition, often left untreated. In the outpatient setting, general practitioners fail to recognize d...

Select or adjust? How information from early treatment stages boosts the prediction of non-response in internet-based depression treatment.

BACKGROUND: Internet-based interventions produce comparable effectiveness rates as face-to-face therapy in treating depression. Still, more than half ...

A feasibility study of robot-assisted percutaneous reduction and fixation technique for treating posterolateral depression tibial plateau fractures.

Posterolateral (PL)-depression fractures of the tibial plateau are difficult to manage. The aim of this study was: (1) to present our experience with ...

Development of depression assessment tools using humanoid robots -Can tele-operated robots talk with depressive persons like humans?

BACKGROUND: Depression is a common mental disorder and causes significant social loss. Early intervention for depression is important. Nonetheless, de...

Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks.

Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. Howev...

Predicting Outcomes of Antidepressant Treatment in Community Practice Settings.

OBJECTIVE: The authors examined whether machine-learning models could be used to analyze data from electronic health records (EHRs) to predict patient...

Evaluation of deep learning-based depression detection using medical claims data.

Human accuracy in diagnosing psychiatric disorders is still low. Even though digitizing health care leads to more and more data, the successful adopti...

An EEG-based marker of functional connectivity: detection of major depressive disorder.

Major depressive disorder (MDD) is a prevalent psychiatric disorder globally. There are many assays for MDD, but rapid and reliable detection remains ...

Effects of Transcranial Direct Current Stimulation on Cognitive Deficits in Depression: A Systematic Review.

BACKGROUND: Major depressive disorder is the leading cause of mental health-related burden globally and up to one-third of major depressive disorder p...

Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach.

BACKGROUND: Depression and momentary depressive feelings are major public health concerns imposing a substantial burden on both individuals and societ...

Deep learning for the prediction of clinical outcomes in internet-delivered CBT for depression and anxiety.

In treating depression and anxiety, just over half of all clients respond. Monitoring and obtaining early client feedback can allow for rapidly adapte...

A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms.

INTRODUCTION: Depression is the leading cause of worldwide disability, until now only 3% of patients with major depressive disorder (MDD) experiences ...

Depression Detection on Social Media: A Classification Framework and Research Challenges and Opportunities.

Social media has become a safe space for discussing sensitive topics such as mental disorders. Depression dominates mental disorders globally, and acc...

A systematic review on automated clinical depression diagnosis.

Assessing mental health disorders and determining treatment can be difficult for a number of reasons, including access to healthcare providers. Assess...

Depression detection for twitter users using sentiment analysis in English and Arabic tweets.

Since depression often results in suicidal thoughts and leaves a person severely disabled daily, there is an elevated risk of premature mortality due ...

A bidirectional thermal sensory leaky integrate-and-fire (LIF) neuron model based on bipolar NbO volatile threshold devices with ultra-low operating current.

Brain-like artificial intelligence (AI) will become the main form and important platform in future computing. It will play an important and unique rol...