Psychiatry

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

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EEG microstate features for schizophrenia classification.

Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is seg...

Prediction of functional outcomes of schizophrenia with genetic biomarkers using a bagging ensemble machine learning method with feature selection.

Genetic variants such as single nucleotide polymorphisms (SNPs) have been suggested as potential mol...

Deep learning applied to electroencephalogram data in mental disorders: A systematic review.

In recent medical research, tremendous progress has been made in the application of deep learning (D...

Commentary: the ethical challenges of machine learning in psychiatry: a focus on data, diagnosis, and treatment.

The clinical interview is the psychiatrist's data gathering procedure. However, the clinical intervi...

[Machine learning and suicide prevention: is an algorithm the solution?].

Suicide is inherently difficult to predict. Epidemiological research identified many general risk fa...

Spatio-temporal graph convolutional network for diagnosis and treatment response prediction of major depressive disorder from functional connectivity.

The pathophysiology of major depressive disorder (MDD) has been explored to be highly associated wit...

Understanding importance of clinical biomarkers for diagnosis of anxiety disorders using machine learning models.

Anxiety disorders are a group of mental illnesses that cause constant and overwhelming feelings of a...

A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans.

Stress is the most prevailing and global psychological condition that inevitably disrupts the mood a...

Pain Assessment Tool With Electrodermal Activity for Postoperative Patients: Method Validation Study.

BACKGROUND: Accurate, objective pain assessment is required in the health care domain and clinical s...

Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews.

Digital data sources have become ubiquitous in modern culture in the era of digital technology but ...

Machine Learning and Natural Language Processing in Mental Health: Systematic Review.

BACKGROUND: Machine learning systems are part of the field of artificial intelligence that automatic...

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.

Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reac...

The Translational Machine: A novel machine-learning approach to illuminate complex genetic architectures.

The Translational Machine (TM) is a machine learning (ML)-based analytic pipeline that translates ge...

Q-CHAT-NAO: A robotic approach to autism screening in toddlers.

The use of humanoid robots as assistants in therapy processes is not new. Several projects in the pa...

Humanoid socially assistive robots in dementia care: a qualitative study about expectations of caregivers and dementia trainers.

OBJECTIVE: To examine the expectations of informal caregivers, nurses, and dementia trainers regardi...

Detection of Negative Stress through Spectral Features of Electroencephalographic Recordings and a Convolutional Neural Network.

In recent years, electroencephalographic (EEG) signals have been intensively used in the area of emo...

Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times.

Major depressive disorder (MDD) is the most common mental disorder in the present day as all individ...

A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network.

Obstructive sleep apnea (OSA) is a common chronic sleep disorder that disrupts breathing during slee...

Prediction of Genotype Positivity in Patients With Hypertrophic Cardiomyopathy Using Machine Learning.

BACKGROUND: Genetic testing can determine family screening strategies and has prognostic and diagnos...

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