Depression

Chatbot-Delivered Psychotherapy for Adults With Depressive and Anxiety Symptoms: A Systematic Review and Meta-Regression.

Although psychotherapy is a well-established treatment for depression and anxiety, chatbot-delivered psychotherapy is an emerging field that has yet to be explored in depth. This review aims to (a) examine the effectiveness of chatbot-delivered psychotherapy in improving depressive symptoms among adults with depression or anxiety, and (b) evaluate the preferred features for the design of chatbot-d...

Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis.

BACKGROUND: Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.

The effects of a sleep robot intervention on sleep, depression and anxiety in adults with insomnia - Study protocol of a randomized waitlist-controlled trial.

Insomnia is a common sleep disorder characterized by difficulties initiating sleep, maintaining sleep and/or early-morning awakenings. Hyperarousal is...

Artificial neural network and decision tree models of post-stroke depression at 3 months after stroke in patients with BMI ≥ 24.

OBJECTIVE: Previous studies have shown that excess weight (including obesity and overweight) can increase the risk of cardiovascular, cerebrovascular ...

Machine Learning: An Overview and Applications in Pharmacogenetics.

This narrative review aims to provide an overview of the main Machine Learning (ML) techniques and their applications in pharmacogenetics (such as ant...

Machine learning model for predicting Major Depressive Disorder using RNA-Seq data: optimization of classification approach.

Considering human brain disorders, Major Depressive Disorder (MDD) is seen as a lethal disease in which a person goes to the extent of suicidal behavi...

Repurposing non-oncology small-molecule drugs to improve cancer therapy: Current situation and future directions.

Drug repurposing or repositioning has been well-known to refer to the therapeutic applications of a drug for another indication other than it was orig...

Antidepressant-like effect of a seeds in mice: Involvement of the monoaminergic system.

BACKGROUND AND AIM: Berg. (Myrtaceae) present several pharmacological actions, but there are no reports on its antidepressant-like potential. This st...

I, robot: depression plays different roles in human-human and human-robot interactions.

Socially engaging robots have been increasingly applied to alleviate depressive symptoms and to improve the quality of social life among different pop...

COVID-19 sentiment analysis via deep learning during the rise of novel cases.

Social scientists and psychologists take interest in understanding how people express emotions and sentiments when dealing with catastrophic events su...

Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy.

Depression symptom heterogeneity limits the identifiability of treatment-response biomarkers. Whether improvement along dimensions of depressive sympt...

Deep graph neural network-based prediction of acute suicidal ideation in young adults.

Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as for psychiatric well-being. Usi...

Machine learning as the new approach in understanding biomarkers of suicidal behavior.

In psychiatry, compared to other medical fields, the identification of biological markers that would complement current clinical interview, and enable...

Detecting suicidal risk using MMPI-2 based on machine learning algorithm.

Minnesota Multiphasic Personality Inventory-2 (MMPI-2) is a widely used tool for early detection of psychological maladjustment and assessing the leve...

Multi-Modal Adaptive Fusion Transformer Network for the Estimation of Depression Level.

Depression is a severe psychological condition that affects millions of people worldwide. As depression has received more attention in recent years, i...

From everyday life predictions to suicide prevention: Clinical and ethical considerations in suicide predictive analytic tools.

Advances in artificial intelligence and machine learning have fueled growing interest in the application of predictive analytics to identify high-risk...

Reproducibility of automated habenula segmentation via deep learning in major depressive disorder and normal controls with 7 Tesla MRI.

The habenula is one of the most important brain regions for investigating the etiology of psychiatric diseases such as major depressive disorder (MDD)...

Polydopamine coated hypodermic needles as a microextraction device for the determination of tricyclic antidepressants in oral fluid by direct infusion MS/MS.

In-needle microextraction consists of the confinement of the sorbent, by coating or packing, inside a metallic needle. The size of the needles reduces...

Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1.

OBJECTIVES: Antidepressants are first-line treatments for major depressive disorder (MDD), but 40-60% of patients will not respond, hence, predicting ...

A machine learning approach for predicting suicidal thoughts and behaviours among college students.

Suicidal thoughts and behaviours are prevalent among college students. Yet little is known about screening tools to identify students at higher risk. ...