Psychiatry

Prediction Effects of Personal, Psychosocial, and Occupational Risk Factors on Low Back Pain Severity Using Artificial Neural Networks Approach in Industrial Workers.

OBJECTIVES: This study aimed to provide an empirical model of predicting low back pain (LBP) by considering the occupational, personal, and psychological risk factor interactions in workers population employed in industrial units using an artificial neural networks approach.

The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and in...

A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).

Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and...

Machine learning methods to predict child posttraumatic stress: a proof of concept study.

BACKGROUND: The care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), usi...

Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study.

BACKGROUND: Early identification and intervention are imperative for suicide prevention. However, at-risk people often neither seek help nor take prof...

Using machine learning and surface reconstruction to accurately differentiate different trajectories of mood and energy dysregulation in youth.

Difficulty regulating positive mood and energy is a feature that cuts across different pediatric psychiatric disorders. Yet, little is known regarding...

Using a social robot to teach gestural recognition and production in children with autism spectrum disorders.

While it has been argued that children with autism spectrum disorders are responsive to robot-like toys, very little research has examined the impact ...

Detecting Abnormal Word Utterances in Children With Autism Spectrum Disorders: Machine-Learning-Based Voice Analysis Versus Speech Therapists.

Abnormal prosody is often evident in the voice intonations of individuals with autism spectrum disorders. We compared a machine-learning-based voice a...

Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective.

Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches ...

Feasibility study of a randomised controlled trial to investigate the effectiveness of using a humanoid robot to improve the social skills of children with autism spectrum disorder (Kaspar RCT): a study protocol.

INTRODUCTION: Interventions using robot-assisted therapy may be beneficial for the social skills development of children with autism spectrum disorder...

Predicting mental conditions based on "history of present illness" in psychiatric notes with deep neural networks.

BACKGROUND: Applications of natural language processing to mental health notes are not common given the sensitive nature of the associated narratives....

A hybrid approach to automatic de-identification of psychiatric notes.

De-identification, or identifying and removing protected health information (PHI) from clinical data, is a critical step in making clinical data avail...

Abnormal regional homogeneity as a potential imaging biomarker for adolescent-onset schizophrenia: A resting-state fMRI study and support vector machine analysis.

OBJECTIVE: Structural and functional abnormalities have been reported in the brain of patients with adolescent-onset schizophrenia (AOS). The brain re...

BrainAGE score indicates accelerated brain aging in schizophrenia, but not bipolar disorder.

BrainAGE (brain age gap estimation) is a novel morphometric parameter providing a univariate score derived from multivariate voxel-wise analyses. It u...

Crowdsourced validation of a machine-learning classification system for autism and ADHD.

Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) together affect >10% of the children in the United States, but cons...

Ordinal convolutional neural networks for predicting RDoC positive valence psychiatric symptom severity scores.

BACKGROUND: The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participan...

Auditory prediction errors as individual biomarkers of schizophrenia.

Schizophrenia is a complex psychiatric disorder, typically diagnosed through symptomatic evidence collected through patient interview. We aim to devel...

Social skills training for children with autism spectrum disorder using a robotic behavioral intervention system.

We designed a robot system that assisted in behavioral intervention programs of children with autism spectrum disorder (ASD). The eight-session interv...

Using short-range and long-range functional connectivity to identify schizophrenia with a family-based case-control design.

Abnormal short-range and long-range functional connectivities (FCs) have been implicated in the neurophysiology of schizophrenia. This study was condu...

Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis.

A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with sc...