Psychiatry

Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity.

OBJECTIVE: Attention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its diagnosis is still confronted with many problems.

Estimation and evaluation of linear individualized treatment rules to guarantee performance.

In clinical practice, an informative and practically useful treatment rule should be simple and transparent. However, because simple rules are likely to be far from optimal, effective methods to construct such rules must guarantee performance, in terms of yielding the best clinical outcome (highest reward) among the class of simple rules under consideration. Furthermore, it is important to evaluat...

Meaningless comparisons lead to false optimism in medical machine learning.

A new trend in medicine is the use of algorithms to analyze big datasets, e.g. using everything your phone measures about you for diagnostics or monit...

Type 2 Diabetes Mellitus Is Associated with Social Recognition Memory Deficit and Altered Dopaminergic Neurotransmission in the Amygdala.

OBJECTIVE: Diabetic neuropathy is a chronic and often disabling condition that affects a significant number of individuals with diabetes mellitus (DM)...

Individualized prediction of psychosis in subjects with an at-risk mental state.

Early intervention strategies in psychosis would significantly benefit from the identification of reliable prognostic biomarkers. Pattern classificati...

Depression recognition according to heart rate variability using Bayesian Networks.

BACKGROUND: Doctors mainly use scale tests and subjective judgment in the clinical diagnosis of depression. Researches have demonstrated that depressi...

Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing.

To summarize recent research and present a selection of the best papers published in 2016 in the field of clinical Natural Language Processing (NLP)....

Predicting posttraumatic stress disorder following a natural disaster.

Earthquakes are a common and deadly natural disaster, with roughly one-quarter of survivors subsequently developing posttraumatic stress disorder (PTS...

Examining Thematic Similarity, Difference, and Membership in Three Online Mental Health Communities from Reddit: A Text Mining and Visualization Approach.

OBJECTIVES: Social media, including online health communities, have become popular platforms for individuals to discuss health challenges and exchange...

Getting RID of the blues: Formulating a Risk Index for Depression (RID) using structural equation modeling.

OBJECTIVE: While risk factors for depression are increasingly known, there is no widely utilised depression risk index. Our objective was to develop a...

Identification of autism spectrum disorder using deep learning and the ABIDE dataset.

The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging datas...

Predicting clinical symptoms of attention deficit hyperactivity disorder based on temporal patterns between and within intrinsic connectivity networks.

Attention deficit hyperactivity disorder (ADHD) is a common brain disorder with high prevalence in school-age children. Previously developed machine l...

A state-independent network of depressive, negative and positive symptoms in male patients with schizophrenia spectrum disorders.

Depressive symptoms occur frequently in patients with schizophrenia. Several factor analytical studies investigated the associations between positive,...

Applying machine learning to identify autistic adults using imitation: An exploratory study.

Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repe...

A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals.

BACKGROUND: Linguistic analysis of publicly available Twitter feeds have achieved success in differentiating individuals who self-disclose online as h...

Global and local excitation and inhibition shape the dynamics of the cortico-striatal-thalamo-cortical pathway.

The cortico-striatal-thalamo-cortical (CSTC) pathway is a brain circuit that controls movement execution, habit formation and reward. Hyperactivity in...

A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support.

BACKGROUND: Probabilistic assessments of clinical care are essential for quality care. Yet, machine learning, which supports this care process has bee...

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media.

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on soc...

Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.

BACKGROUND: There are no consistent predictors of treatment outcome in paediatric obsessive-compulsive disorder (OCD). One reason for this might be th...

Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression.

BACKGROUND: We report a study of machine learning applied to the phenotyping of psychiatric diagnosis for research recruitment in youth depression, co...