Psychiatry

Latest AI and machine learning research in psychiatry for healthcare professionals.

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Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important.

Combining neuroimaging and clinical information for diagnosis, as for example behavioral tasks and g...

Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach.

BACKGROUND: Medications are frequently used for treating schizophrenia, however, anti-psychotic drug...

Artificial intelligence based discovery of the association between depression and chronic fatigue syndrome.

BACKGROUND: Both of the modern medicine and the traditional Chinese medicine classify depressive dis...

Predicting one-year outcome in first episode psychosis using machine learning.

BACKGROUND: Early illness course correlates with long-term outcome in psychosis. Accurate prediction...

Job interview training targeting nonverbal communication using an android robot for individuals with autism spectrum disorder.

Job interviews are significant barriers for individuals with autism spectrum disorder because these ...

Modelling PTSD diagnosis using sleep, memory, and adrenergic metabolites: An exploratory machine-learning study.

OBJECTIVE: Features of posttraumatic stress disorder (PTSD) typically include sleep disturbances, im...

Robust water-fat separation for multi-echo gradient-recalled echo sequence using convolutional neural network.

PURPOSE: To accurately separate water and fat signals for bipolar multi-echo gradient-recalled echo ...

Machine Learning, Natural Language Processing, and the Electronic Health Record: Innovations in Mental Health Services Research.

An unprecedented amount of clinical information is now available via electronic health records (EHRs...

EEG-based mild depression recognition using convolutional neural network.

Electroencephalography (EEG)-based studies focus on depression recognition using data mining methods...

Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder.

Brain imaging studies have revealed that functional and structural brain connectivity in the so-call...

Deep neural networks in psychiatry.

Machine and deep learning methods, today's core of artificial intelligence, have been applied with i...

The Early Psychosis Screener for Internet (EPSI)-SR: Predicting 12 month psychotic conversion using machine learning.

INTRODUCTION: A faster and more accurate self-report screener for early psychosis is needed to promo...

A randomized controlled trial of suicide prevention training for primary care providers: a study protocol.

BACKGROUND: Suicide is a national public health crisis and a critical patient safety issue. It is th...

Machine learning in mental health: a scoping review of methods and applications.

BACKGROUND: This paper aims to synthesise the literature on machine learning (ML) and big data appli...

Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks.

In recent years, there is a rapid increase in the population of elderly people. However, elderly peo...

Electroconvulsive Therapy Induces Cortical Morphological Alterations in Major Depressive Disorder Revealed with Surface-Based Morphometry Analysis.

Although electroconvulsive therapy (ECT) is one of the most effective treatments for major depressiv...

Predicting inadequate postoperative pain management in depressed patients: A machine learning approach.

Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to ...

Neurodevelopmental heterogeneity and computational approaches for understanding autism.

In recent years, the emerging field of computational psychiatry has impelled the use of machine lear...

Outcome-Weighted Learning for Personalized Medicine with Multiple Treatment Options.

To achieve personalized medicine, an individualized treatment strategy assigning treatment based on ...

Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death.

BACKGROUND: Identification of individuals at increased risk for suicide is an important public healt...

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