Psychiatry

Exploring the Ethical Challenges of Conversational AI in Mental Health Care: Scoping Review.

BACKGROUND: Conversational artificial intelligence (CAI) is emerging as a promising digital technology for mental health care. CAI apps, such as psychotherapeutic chatbots, are available in app stores, but their use raises ethical concerns.

Identifying major depressive disorder among US adults living alone using stacked ensemble machine learning algorithms.

BACKGROUND: It has been increasingly recognized that adults living alone have a higher likelihood of developing Major Depressive Disorder (MDD) than those living with others. However, there is still no prediction model for MDD specifically designed for adults who live alone.

Proteome analysis of the prefrontal cortex and the application of machine learning models for the identification of potential biomarkers related to suicide.

INTRODUCTION: Suicide is a significant public health problem, with increased rates in low- and middle-income countries such as Mexico; therefore, suic...

Improving unified information extraction in Chinese mental health domain with instruction-tuned LLMs and type-verification component.

BACKGROUND: Extracting psychological counseling help-seeker information from unstructured text is crucial for providing effective mental health suppor...

Integrative diagnosis of psychiatric conditions using ChatGPT and fMRI data.

BACKGROUND: Traditional diagnostic methods for psychiatric disorders often rely on subjective assessments, leading to inconsistent diagnoses. Integrat...

Development and validation of machine learning models based on stacked generalization to predict psychosocial maladjustment in patients with acute myocardial infarction.

BACKGROUND: Psychosocial maladjustment threatens the recovery of patients with acute myocardial infarction (AMI), and early identification of patients...

Predicting PTSD development with early post-trauma assessments: a proof-of-concept for a concise tree-based classification method.

Approximately 70% of individuals globally experience at least one traumatic event in their lifetimes, potentially leading to posttraumatic stress dis...

Predicting Treatment Response of Repetitive Transcranial Magnetic Stimulation in Major Depressive Disorder Using an Explainable Machine Learning Model Based on Electroencephalography and Clinical Features.

Major depressive disorder (MDD) is highly heterogeneous in response to repetitive transcranial magnetic stimulation (rTMS), and identifying predictive...

A systematic literature review of machine learning techniques for the detection of attention-deficit/hyperactivity disorder using MRI and/or EEG data.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition common in teenagers across the globe. Neuroimaging and Machine Learn...

Predicting mental health disparities using machine learning for African Americans in Southeastern Virginia.

This study examined mental health disparities among African Americans using AI and machine learning for outcome prediction. Analyzing data from Africa...

Efficient Neural Network Classification of Parkinson's Disease and Schizophrenia Using Resting-State EEG Data.

Timely identification of Parkinson's disease and schizophrenia is crucial for the effective management and enhancement of patients' quality of life. T...

Interpretable machine learning approaches for children's ADHD detection using clinical assessment data: an online web application deployment.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental disorder characterized by hyperactivity, impulsivity, and inattentio...

Prediction of depressive disorder using machine learning approaches: findings from the NHANES.

BACKGROUND: Depressive disorder, particularly major depressive disorder (MDD), significantly impact individuals and society. Traditional analysis meth...

Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation.

BACKGROUND: The changes in DNA methylation patterns may reflect both physical and mental well-being, the latter being a relatively unexplored avenue i...

Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline.

While numerous studies strive to exploit the complementary potential of MRI and PET using learning-based methods, the effective fusion of the two moda...

Predicting antipsychotic responsiveness using a machine learning classifier trained on plasma levels of inflammatory markers in schizophrenia.

We apply machine learning techniques to navigate the multifaceted landscape of schizophrenia. Our method entails the development of predictive models,...

The application of artificial intelligence in the field of mental health: a systematic review.

INTRODUCTION: The integration of artificial intelligence in mental health care represents a transformative shift in the identification, treatment, and...

Comparison of logistic regression and machine learning methods for predicting depression risks among disabled elderly individuals: results from the China Health and Retirement Longitudinal Study.

BACKGROUND: Given the accelerated aging population in China, the number of disabled elderly individuals is increasing, and depression is a common ment...

Exploring Potential Medications for Alzheimer's Disease with Psychosis by Integrating Drug Target Information into Deep Learning Models: A Data-Driven Approach.

Approximately 50% of Alzheimer's disease (AD) patients develop psychotic symptoms, leading to a subtype known as psychosis in AD (AD + P), which is as...

Differentiating adolescent suicidal and nonsuicidal self-harm with artificial intelligence: Beyond suicidal intent and capability for suicide.

Clinical differentiation between adolescent suicidal self-harm (SSH) and nonsuicidal self-harm (NSSH) is a significant challenge for mental health pro...