AIMC Topic: Depressive Disorder, Major

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Optimizing precision medicine for second-step depression treatment: a machine learning approach.

Psychological medicine
BACKGROUND: Less than a third of patients with depression achieve successful remission with standard first-step antidepressant monotherapy. The process for determining appropriate second-step care is often based on clinical intuition and involves a p...

Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal.

Physical and engineering sciences in medicine
In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) pa...

DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Major Depression Disorder (MDD) is a common yet destructive mental disorder that affects millions of people worldwide. Making early and accurate diagnosis of it is very meaningful. Recently, EEG, a non-invasive technique of recording spontaneous elec...

Interpretable deep learning model for major depressive disorder assessment based on functional near-infrared spectroscopy.

Asian journal of psychiatry
BACKGROUND: Major depressive disorder (MDD) affects a substantial number of individuals worldwide. New approaches are required to improve the diagnosis of MDD, which relies heavily on subjective reports of depression-related symptoms.

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

Journal of psychiatric research
BACKGROUND: Depression is a common mental disorder and causes significant social loss. Early intervention for depression is important. Nonetheless, depressed patients tend to conceal their symptoms from others based on shame and stigma, thus hesitate...

Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers.

Journal of affective disorders
BACKGROUND: Mood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing trea...

Use of mobile technology to identify behavioral mechanisms linked to mental health outcomes in Kenya: protocol for development and validation of a predictive model.

BMC research notes
OBJECTIVE: This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode and future mood among healthcare workers in Kenya.

Machine Learning and Electroencephalogram Signal based Diagnosis of Dipression.

Neuroscience letters
Depression is a psychological condition which hampers day to day activity (Thinking, Feeling or Action). The early detection of this illness will help to save many lives because it is now recognized as a global problem which could even lead to suicid...

Identifying suicide attempts, ideation, and non-ideation in major depressive disorder from structural MRI data using deep learning.

Asian journal of psychiatry
The present study aims to identify suicide risks in major depressive disorders (MDD) patients from structural MRI (sMRI) data using deep learning. In this paper, we collected the sMRI data of 288 MDD patients, including 110 patients with suicide idea...

A Model of Normality Inspired Deep Learning Framework for Depression Relapse Prediction Using Audiovisual Data.

Computer methods and programs in biomedicine
BACKGROUND: Depression (Major Depressive Disorder) is one of the most common mental illnesses. According to the World Health Organization, more than 300 million people in the world are affected. A first depressive episode can be solved by a spontaneo...