Psychiatry

Good News: Artificial Intelligence in Psychiatry Is Actually Neither.

Assessing various sensorimotor and cognitive functions in people with epilepsy is feasible with robotics.

BACKGROUND: Epilepsy is a common neurological disorder characterized by recurrent seizures, along wi...

Adaptive feature extraction in EEG-based motor imagery BCI: tracking mental fatigue.

OBJECTIVE: Electroencephalogram (EEG) signals are non-stationary. This could be due to internal fluc...

Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression.

IMPORTANCE: Social and economic costs of depression are exacerbated by prolonged periods spent ident...

A Multidimensional Neural Maturation Index Reveals Reproducible Developmental Patterns in Children and Adolescents.

Adolescence is a time of extensive neural restructuring, leaving one susceptible to atypical develop...

Seeking for potential pathogenic genes of major depressive disorder in the Gene Expression Omnibus database.

INTRODUCTION: Major depressive disorder (MDD) is one of the most common mental disorders worldwide. ...

Using machine learning to model problematic smartphone use severity: The significant role of fear of missing out.

We examined a model of psychopathology variables, age and sex as correlates of problematic smartphon...

Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms.

To improve the prediction of the individual risk of conversion to psychosis in UHR subjects, by cons...

Ensemble Learning for Early-Response Prediction of Antidepressant Treatment in Major Depressive Disorder.

BACKGROUND: In order to reduce unsuccessful treatment trials for depression, neuroimaging and geneti...

Shall I compare thee…to a robot? An exploratory pilot study using participatory arts and social robotics to improve psychological well-being in later life.

Research suggests that the use of creative, artistic activities in later life may positively impact ...

Predicting cognitive behavioral therapy outcome in the outpatient sector based on clinical routine data: A machine learning approach.

The availability of large-scale datasets and sophisticated machine learning tools enables developing...

Predicting death by suicide using administrative health care system data: Can recurrent neural network, one-dimensional convolutional neural network, and gradient boosted trees models improve prediction performance?

BACKGROUND: Suicide is a leading cause of death, particularly in younger persons, and this results i...

Data-Driven Diagnostics and the Potential of Mobile Artificial Intelligence for Digital Therapeutic Phenotyping in Computational Psychiatry.

Data science and digital technologies have the potential to transform diagnostic classification. Dig...

Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry.

Depression or Major Depressive Disorder (MDD) is a mental illness which negatively affects how a per...

Multiple Holdouts With Stability: Improving the Generalizability of Machine Learning Analyses of Brain-Behavior Relationships.

BACKGROUND: In 2009, the National Institute of Mental Health launched the Research Domain Criteria, ...

Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant.

Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of ch...

Decoding rumination: A machine learning approach to a transdiagnostic sample of outpatients with anxiety, mood and psychotic disorders.

OBJECTIVE: To employ machine learning algorithms to examine patterns of rumination from RDoC perspec...

Developing algorithms to predict adult onset internalizing disorders: An ensemble learning approach.

A growing literature is utilizing machine learning methods to develop psychopathology risk algorithm...

The use of machine learning techniques in trauma-related disorders: a systematic review.

Establishing the diagnosis of trauma-related disorders such as Acute Stress Disorder (ASD) and Postt...

Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) statement.

Spatial lifecourse epidemiology is an interdisciplinary field that utilizes advanced spatial, locati...

Characterizing autism spectrum disorder by deep learning spontaneous brain activity from functional near-infrared spectroscopy.

BACKGROUND: Functional near-infrared spectroscopy (fNIRS) was used to investigate spontaneous hemody...