Psychiatry

Predicting the trajectory of non-suicidal self-injury among adolescents.

BACKGROUND: Non-suicidal self-injury (NSSI) is common among adolescents receiving inpatient psychiatric treatment and the months post-discharge is a high-risk period for self-injurious behavior. Thus, identifying predictors that shape the course of post-discharge NSSI may provide insights into ways to improve clinical outcomes. Accordingly, we used machine learning to identify the strongest predic...

Gender-specific factors of suicidal ideation among high school students in Yunnan province, China: A machine learning approach.

BACKGROUND: Suicidal ideation (SI) assumes a pivotal role in predicting suicidal behaviors. The incidence of SI among high (junior and senior) school students is significantly higher than that of other age groups. The aim of this study is to explore the gender differences in SI among high school students in Yunnan Province.

Age-stratified predictions of suicide attempts using machine learning in middle and late adolescence.

BACKGROUND: Prevalence of suicidal behaviour increases rapidly in middle to late adolescence. Predicting suicide attempts across different ages would ...

Identifying momentary suicidal ideation using machine learning in patients at high-risk for suicide.

BACKGROUND: Strategies to detect the presence of suicidal ideation (SI) or characteristics of ideation that indicate marked suicide risk are criticall...

Predictive roles of cognitive biases in health anxiety: A machine learning approach.

Prior work suggests that cognitive biases may contribute to health anxiety. Yet there is little research investigating how biased attention, interpret...

Multilevel hybrid handcrafted feature extraction based depression recognition method using speech.

BACKGROUND AND PURPOSE: Diagnosis of depression is based on tests performed by psychiatrists and information provided by patients or their relatives. ...

Global Research on Pandemics or Epidemics and Mental Health: A Natural Language Processing Study.

BACKGROUND: The global research on pandemics or epidemics and mental health has been growing exponentially recently, which cannot be integrated throug...

Identification of Psychological Treatment Dropout Predictors Using Machine Learning Models on Italian Patients Living with Overweight and Obesity Ineligible for Bariatric Surgery.

According to the main international guidelines, patients with obesity and psychiatric/psychological disorders who cannot be addressed to surgery are r...

Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.

Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics a...

Unraveling the physiological and psychosocial signatures of pain by machine learning.

BACKGROUND: Pain is a complex subjective experience, strongly impacting health and quality of life. Despite many attempts to find effective solutions,...

Development of an eye-tracking system based on a deep learning model to assess executive function in patients with mental illnesses.

Patients with mental illnesses, particularly psychosis and obsessive‒compulsive disorder (OCD), frequently exhibit deficits in executive function and ...

A Novel Unsupervised Machine Learning Approach to Assess Postural Dynamics in Euthymic Bipolar Disorder.

Bipolar disorder (BD) is a mood disorder with different phases alternating between euthymia, manic or hypomanic episodes, and depressive episodes. Whi...

Enhancing Major Depressive Disorder Diagnosis With Dynamic-Static Fusion Graph Neural Networks.

Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms hindering diagnostic progress. Research links MDD ...

EEG based depression detection by machine learning: Does inner or overt speech condition provide better biomarkers when using emotion words as experimental cues?

BACKGROUND: Objective diagnostic approaches need to be tested to enhance the efficacy of depression detection. Non-invasive EEG-based identification r...

Enhancing schizophrenia phenotype prediction from genotype data through knowledge-driven deep neural network models.

This article explores deep learning model design, drawing inspiration from the omnigenic model and genetic heterogeneity concepts, to improve schizoph...

Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study.

BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental psychiatric condition that typically emerges during c...

Assessing dimensions of thought disorder with large language models: The tradeoff of accuracy and consistency.

Natural Language Processing (NLP) methods have shown promise for the assessment of formal thought disorder, a hallmark feature of schizophrenia in whi...

Scoping review on natural language processing applications in counselling and psychotherapy.

Recent years have witnessed some rapid and tremendous progress in natural language processing (NLP) techniques that are used to analyse text data. Thi...

Predicting the Hallucinogenic Potential of Molecules Using Artificial Intelligence.

The development of new drugs addressing serious mental health and other disorders should avoid the psychedelic experience. Analogs of psychedelic drug...

Unlocking treatment success: predicting atypical antipsychotic continuation in youth with mania.

PURPOSE: This study aimed to create and validate robust machine-learning-based prediction models for antipsychotic drug (risperidone) continuation in ...