Psychiatry

Integrating exploration and prediction in computational psychotherapy science: proof of concept.

INTRODUCTION: Psychotherapy research has long preferred explanatory over predictive models. As a result, psychotherapy research is currently limited in the variability that can be accounted for in the process and outcome of treatment. The present study is a proof-of-concept approach to psychotherapy science that uses a datadriven approach to achieve robust predictions of the process and outcome of...

Illusory generalizability of clinical prediction models.

It is widely hoped that statistical models can improve decision-making related to medical treatments. Because of the cost and scarcity of medical outcomes data, this hope is typically based on investigators observing a model's success in one or two datasets or clinical contexts. We scrutinized this optimism by examining how well a machine learning model performed across several independent clinica...

Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning.

BACKGROUND: Traditional approaches to modeling suicide-related thoughts and behaviors focus on few data types from often-siloed disciplines. While psy...

Bridging the Skills Gap: Evaluating an AI-Assisted Provider Platform to Support Care Providers with Empathetic Delivery of Protocolized Therapy.

Despite the high prevalence and burden of mental health conditions, there is a global shortage of mental health providers. Artificial Intelligence (AI...

Application of Hybrid DeepLearning Architectures for Identification of Individuals with Obsessive Compulsive Disorder Based on EEG Data.

Obsessive-compulsive disorder (OCD) is a highly common psychiatric disorder. The symptoms of this condition overlap and co-occur with those of other ...

Prediction of adverse events risk in patients with comorbid post-traumatic stress disorder and alcohol use disorder using electronic medical records by deep learning models.

BACKGROUND: Identifying co-occurring mental disorders and elevated risk is vital for optimization of healthcare processes. In this study, we will use ...

Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public.

BACKGROUND: Artificial intelligence (AI) has rapidly permeated various sectors, including healthcare, highlighting its potential to facilitate mental ...

A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study.

OBJECTIVE: Depression is very common and harmful in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). It is necessary to screen OSAHS p...

Exploring White Matter Abnormalities in Young Children with Autism Spectrum Disorder: Integrating Multi-shell Diffusion Data and Machine Learning Analysis.

RATIONALE AND OBJECTIVES: This study employed tract-based spatial statistics (TBSS) to investigate abnormalities in the white matter microstructure am...

Seeing through a robot's eyes: A cross-sectional exploratory study in developing a robotic screening technology for autism.

The present exploratory cross-sectional case-control study sought to develop a reliable and scalable screening tool for autism using a social robot. T...

Easing the Burden on Caregivers- Applications of Artificial Intelligence for Physicians and Caregivers of Children with Cleft Lip and Palate.

ObjectiveMany caregivers of children with cleft lip and palate experience a high level of anxiety throughout their child's medical and surgical care. ...

Exploring the impact of human-robot interaction on workers' mental stress in collaborative assembly tasks.

Advances in robotics have contributed to the prevalence of human-robot collaboration (HRC). However, working and interacting with collaborative robots...

Editorial: Psychometrics in psychiatry 2022: anxiety and stress disorders.

Control list of high-priority chemicals based on 5-HT-RI functionality and the human health interference effects selective CNN-GRU deep learning model.

The antidepressant drug known as 5-HT reuptake inhibitor (5-HT-RI) was commonly detected in biological tissues and result in significant adverse healt...

A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and inter...

Development of depression detection algorithm using text scripts of routine psychiatric interview.

BACKGROUND: A psychiatric interview is one of the important procedures in diagnosing psychiatric disorders. Through this interview, psychiatrists list...

ChatGPT is not ready yet for use in providing mental health assessment and interventions.

BACKGROUND: Psychiatry is a specialized field of medicine that focuses on the diagnosis, treatment, and prevention of mental health disorders. With ad...

Enabling Lipidomic Biomarker Studies for Protected Populations by Combining Noninvasive Fingerprint Sampling with MS Analysis and Machine Learning.

Triacylglycerols and wax esters are two lipid classes that have been linked to diseases, including autism, Alzheimer's disease, dementia, cardiovascul...

Interpretable deep learning model for major depressive disorder assessment based on functional near-infrared spectroscopy.

BACKGROUND: Major depressive disorder (MDD) affects a substantial number of individuals worldwide. New approaches are required to improve the diagnosi...

The performance of bibliometric analyses in the health sciences.

A bibliometric analysis (BA) is a knowledge synthesis methodology aimed at quantitively summarizing large amounts of bibliometric data. We aimed to su...