Psychiatry

Canada protocol: An ethical checklist for the use of artificial Intelligence in suicide prevention and mental health.

A deep learning model for detecting mental illness from user content on social media.

Users of social media often share their feelings or emotional states through their posts. In this study, we developed a deep learning model to identify a user's mental state based on his/her posting information. To this end, we collected posts from mental health communities in Reddit. By analyzing and learning posting information written by users, our proposed model could accurately identify wheth...

Deep learning for small and big data in psychiatry.

Psychiatry today must gain a better understanding of the common and distinct pathophysiological mechanisms underlying psychiatric disorders in order t...

Automated design and optimization of multitarget schizophrenia drug candidates by deep learning.

Complex neuropsychiatric diseases such as schizophrenia require drugs that can target multiple G protein-coupled receptors (GPCRs) to modulate complex...

Understanding the relationship between patient language and outcomes in internet-enabled cognitive behavioural therapy: A deep learning approach to automatic coding of session transcripts.

Understanding patient responses to psychotherapy is important in developing effective interventions. However, coding patient language is a resource-i...

A Machine Learning Approach to Understanding Patterns of Engagement With Internet-Delivered Mental Health Interventions.

IMPORTANCE: The mechanisms by which engagement with internet-delivered psychological interventions are associated with depression and anxiety symptoms...

Generalized linear mixed-model (GLMM) trees: A flexible decision-tree method for multilevel and longitudinal data.

Decision-tree methods are machine-learning methods which provide results that are relatively easy to interpret and apply by human decision makers. Th...

A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI.

Segmentation of the hippocampus (HC) in magnetic resonance imaging (MRI) is an essential step for diagnosis and monitoring of several clinical situati...

Machine learning for genetic prediction of psychiatric disorders: a systematic review.

Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outco...

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients.

Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may...

Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure.

BACKGROUND: Though lifetime exposure to traumatic events is significant, only a minority of individuals develops symptoms of posttraumatic stress diso...

On-line anxiety level detection from biosignals: Machine learning based on a randomized controlled trial with spider-fearful individuals.

We present performance results concerning the validation for anxiety level detection based on trained mathematical models using supervised machine lea...

Identifying the Symptom Severity in Obsessive-Compulsive Disorder for Classification and Prediction: An Artificial Neural Network Approach.

The present study is aimed at identifying the most prominent determinants of OCD along with their strength to classify the OCD patients from healthy c...

Computing schizophrenia: ethical challenges for machine learning in psychiatry.

Recent advances in machine learning (ML) promise far-reaching improvements across medical care, not least within psychiatry. While to date no psychiat...

Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action.

Bipolar disorder (BD) is a major healthcare and socio-economic challenge. Despite its substantial burden on society, the research activity in BD is mu...

A preliminary evaluation of still face images by deep learning: A potential screening test for childhood developmental disabilities.

Most developmental disorders are defined by their clinical symptoms and many disorders share common features. The main objective of this research is t...

Deep Support Vector Machines for the Identification of Stress Condition from Electrodermal Activity.

Early detection of stress condition is beneficial to prevent long-term mental illness like depression and anxiety. This paper introduces an accurate i...

Identifying psychosis spectrum youth using support vector machines and cerebral blood perfusion as measured by arterial spin labeled fMRI.

Altered cerebral blood flow (CBF), as measured by arterial spin labelling (ASL), has been observed in several psychiatric conditions, but is a general...

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach.

The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental...

AI in mental health.

With the advent of digital approaches to mental health, modern artificial intelligence (AI), and machine learning in particular, is being used in the ...