Depression

Leveraging Digital Health and Machine Learning Toward Reducing Suicide-From Panacea to Practical Tool.

Elucidating Compound Mechanism of Action and Predicting Cytotoxicity Using Machine Learning Approaches, Taking Prediction Confidence into Account.

The modes of action (MoAs) of drugs frequently are unknown, because many are small molecules initially identified from phenotypic screens, giving rise to the need to elucidate their MoAs. In addition, the high attrition rate for candidate drugs in preclinical studies due to intolerable toxicity has motivated the development of computational approaches to predict drug candidate (cyto)toxicity as ea...

Integration of machine learning and pharmacogenomic biomarkers for predicting response to antidepressant treatment: can computational intelligence be used to augment clinical assessments?

Text Classification to Inform Suicide Risk Assessment in Electronic Health Records.

Assessing a patient's risk of an impending suicide attempt has been hampered by limited information about dynamic factors that change rapidly in the d...

Identifying Suicidal Adolescents from Mental Health Records Using Natural Language Processing.

Suicidal ideation is a risk factor for self-harm, completed suicide and can be indicative of mental health issues. Adolescents are a particularly vuln...

Evaluation of a Companion Robot for Individuals With Dementia: Quantitative Findings of the MARIO Project in an Irish Residential Care Setting.

The current study focuses on the short-term effect of MARIO, a social robot, on quality of life, depression, and perceived social support in persons w...

A Social Media Study on the Effects of Psychiatric Medication Use.

Understanding the effects of psychiatric medications during mental health treatment constitutes an active area of inquiry. While clinical trials help ...

RRAM-based synapse devices for neuromorphic systems.

Hardware artificial neural network (ANN) systems with high density synapse array devices can perform massive parallel computing for pattern recognitio...

Understanding Mood Disorders in Children.

Mood disorders include all types of depression and bipolar disorder, and mood disorders are sometimes called affective disorders. We will discuss newl...

[Machine learning in predicting suicide: something for the future?].

Dexamethasone Versus Ketamine in the Interscalene Block in Patients Undergoing Arthroscopic Shoulder Surgery: A Randomized Double-Blinded Study.

OBJECTIVE: Arthroscopic shoulder surgery (ASS) is often followed by severe pain. Interscalene brachial plexus block (ISB) was used to relieve such pai...

Predictive markers of depression in hypertension.

Hypertension and depression, as 2 major public health issues, are closely related. For patients having hypertension, in particular, depression is a ri...

Classifying Treated vs. Untreated MDD Adolescents from Anatomical Connectivity using Nonlinear SVM.

Identification of the treatment-related responders for adolescent Major Depressive Disorder (MDD) is urgently needed to develop effective treatments. ...

Anatomical Biomarkers for Adolescent Major Depressive Disorder from Diffusion Weighted Imaging using SVM Classifier.

Adolescent Major Depressive Disorder (MDD) is a common and serious mental illness that could lead to tragic outcomes including chronic adult disabilit...

Depression Severity Classification from Speech Emotion.

Major Depressive Disorder (MDD) is a common psychiatric illness. Automatically classifying depression severity using audio analysis can help clinical ...

AI opens new frontier for suicide prevention.

Refining Prediction in Treatment-Resistant Depression: Results of Machine Learning Analyses in the TRD III Sample.

OBJECTIVE: The study objective was to generate a prediction model for treatment-resistant depression (TRD) using machine learning featuring a large se...

Predicting Depression Among Community Residing Older Adults: A Use of Machine Learning Approch.

The study demonstrated an application of machine learning techniques in building a depression prediction model. We used the NSHAP II data (3,377 subje...

Automatic Detection of Depression by Using a Neural Network.

Depression is the most common psychiatric disorder worldwide, which affects more than 300 million people. We aimed to detect depressed patients and he...

Multi-class machine classification of suicide-related communication on Twitter.

The World Wide Web, and online social networks in particular, have increased connectivity between people such that information can spread to millions ...