Psychiatry

Individual prediction of long-term outcome in adolescents at ultra-high risk for psychosis: Applying machine learning techniques to brain imaging data.

An important focus of studies of individuals at ultra-high risk (UHR) for psychosis has been to identify biomarkers to predict which individuals will transition to psychosis. However, the majority of individuals will prove to be resilient and go on to experience remission of their symptoms and function well. The aim of this study was to investigate the possibility of using structural MRI measures ...

Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media.

Social media sites are rapidly becoming one of the most important forums for public deliberation about advocacy issues. However, social scientists have not explained why some advocacy organizations produce social media messages that inspire far-ranging conversation among social media users, whereas the vast majority of them receive little or no attention. I argue that advocacy organizations are mo...

Artificial neural network EMG classifier for functional hand grasp movements prediction.

Objective To design and implement an electromyography (EMG)-based controller for a hand robotic assistive device, which is able to classify the user's...

Novel Use of Natural Language Processing (NLP) to Predict Suicidal Ideation and Psychiatric Symptoms in a Text-Based Mental Health Intervention in Madrid.

Natural language processing (NLP) and machine learning were used to predict suicidal ideation and heightened psychiatric symptoms among adults recentl...

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data.

Machine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require l...

Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder.

Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals i...

Medical Waste Disposal Method Selection Based on a Hierarchical Decision Model with Intuitionistic Fuzzy Relations.

Although medical waste usually accounts for a small fraction of urban municipal waste, its proper disposal has been a challenging issue as it often co...

Predicting Social Anxiety Treatment Outcome Based on Therapeutic Email Conversations.

Predicting therapeutic outcome in the mental health domain is of utmost importance to enable therapists to provide the most effective treatment to a p...

Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry.

Problematic internet use is common, functionally impairing, and in need of further study. Its relationship with obsessive-compulsive and impulsive dis...

Thought-Controlled Nanoscale Robots in a Living Host.

We report a new type of brain-machine interface enabling a human operator to control nanometer-size robots inside a living animal by brain activity. R...

Support vector machine model of developmental brain gene expression data for prioritization of Autism risk gene candidates.

MOTIVATION: Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders with clinical heterogeneity and a substantial polygenic compon...

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical...

Automated Assessment of Patients' Self-Narratives for Posttraumatic Stress Disorder Screening Using Natural Language Processing and Text Mining.

Patients' narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented ...

Accuracy of automated classification of major depressive disorder as a function of symptom severity.

BACKGROUND: Growing evidence documents the potential of machine learning for developing brain based diagnostic methods for major depressive disorder (...

Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine-learning study.

BACKGROUND: Early diagnosis of schizophrenia could improve the outcomes and limit the negative effects of untreated illness. Although participants wit...

Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features.

Recently, an increasing number of researchers have endeavored to develop practical tools for diagnosing patients with schizophrenia using machine lear...

Non-reward neural mechanisms in the orbitofrontal cortex.

Single neurons in the primate orbitofrontal cortex respond when an expected reward is not obtained, and behaviour must change. The human lateral orbit...

Machine learning, statistical learning and the future of biological research in psychiatry.

Psychiatric research has entered the age of 'Big Data'. Datasets now routinely involve thousands of heterogeneous variables, including clinical, neuro...

Atypical Asymmetry for Processing Human and Robot Faces in Autism Revealed by fNIRS.

Deficits in the visual processing of faces in autism spectrum disorder (ASD) individuals may be due to atypical brain organization and function. Studi...

Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.

Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (...