Psychiatry

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

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Toward Automatic Risk Assessment to Support Suicide Prevention.

Suicide has been considered an important public health issue for years and is one of the main cause...

Supervised machine learning to decipher the complex associations between neuro-immune biomarkers and quality of life in schizophrenia.

Stable phase schizophrenia is characterized by altered patterning in tryptophan catabolites (TRYCATs...

Identifying probable post-traumatic stress disorder: applying supervised machine learning to data from a UK military cohort.

BACKGROUND: Early identification of probable post-traumatic stress disorder (PTSD) can lead to early...

The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review.

BACKGROUND: Machine learning techniques offer promise to improve suicide risk prediction. In the cur...

Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application.

BACKGROUND: Electronic health records (EHRs) bring many opportunities for information utilization. O...

Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach.

BACKGROUND: Bipolar disorder (BD) is a severe psychiatric disorder characterized by periodic episode...

Artificial Intelligence in Radiology: Current Technology and Future Directions.

Artificial intelligence (AI) has been heralded as the next big wave in the computing revolution and ...

A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression.

BACKGROUND: Some Internet interventions are regarded as effective treatments for adult depression, b...

Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach.

Many variables have been linked to different course trajectories of depression. These findings, howe...

Social robots to support children's well-being under medical treatment: A systematic state-of-the-art review.

Hospitalization is a stressful experience for children. Socially assistive robots (SARs), designed t...

Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization.

Posttraumatic stress disorder (PTSD) develops in a substantial minority of emergency room admits. In...

Comparative Evaluation of Machine Learning Strategies for Analyzing Big Data in Psychiatry.

The requirement of innovative big data analytics has become a critical success factor for research i...

Individualized prediction of dispositional worry using white matter connectivity.

BACKGROUND: Excessive worry is a defining feature of generalized anxiety disorder and is present in ...

Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances.

The importance of incorporating Natural Language Processing (NLP) methods in clinical informatics re...

Intrinsic motivation and mental replay enable efficient online adaptation in stochastic recurrent networks.

Autonomous robots need to interact with unknown, unstructured and changing environments, constantly ...

Psychiatric stressor recognition from clinical notes to reveal association with suicide.

Suicide takes the lives of nearly a million people each year and it is a tremendous economic burden ...

Assessing the severity of positive valence symptoms in initial psychiatric evaluation records: Should we use convolutional neural networks?

BACKGROUND AND OBJECTIVE: Efficiently capturing the severity of positive valence symptoms could aid ...

Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification.

Mental tasks classification is increasingly recognized as a major challenge in the field of EEG sign...

Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study.

Machine learning is becoming an increasingly popular approach for investigating spatially distribute...

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