Psychiatry

A Dopamine-Serotonin Theory of Consciousness

This work presents a comprehensive theory of consciousness grounded in mathematical formalism and supported by clinical data analysis. The framework developed herein demonstrates that consciousness exists as a continuous, non-monotonic function across a high-dimensional neurochemical space, with dopamine serving as the primary intensity regulator and serotonin (5-HT2A) as the complexity modulato...

Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection

The complexity of mental healthcare billing enables anomalies, including fraud. While machine learning methods have been applied to anomaly detection, they often struggle with class imbalance, label scarcity, and complex sequential patterns. This study explores a hybrid deep learning approach combining Long Short-Term Memory (LSTM) networks and Transformers, with pseudo-labeling via Isolation Fo...

Incorporating end-user perspectives into the development of a machine learning algorithm for first time perinatal depression prediction.

OBJECTIVE: Machine learning algorithms can advance clinical care, including identifying mental health conditions. These algorithms are often developed...

Prenatal exposure to criteria air pollution and traffic-related air toxics and risk of autism spectrum disorder: A population-based cohort study of California births (1990-2018).

BACKGROUND: Autism spectrum disorder (ASD) prevalence has risen steadily in California (CA) over several decades, with environmental factors like air ...

Distinct processing stages of cross-modal conflict in schizophrenia: The role of auditory cortex underactivation.

BACKGROUND: The cross-modal conflict deficit is a key feature of schizophrenia. However, it remains largely unknown whether cross-modal conflict in sc...

Beyond total scores: Enhancing psychotherapy outcome prediction with item-level scores.

OBJECTIVE: This study aims at improving dropout and treatment nonresponse prevention by optimizing the performance of models for their prediction thro...

Artificial intelligence in autism spectrum disorder: A path to early detection and improved outcomes.

Identifying metabolites of new psychoactive substances using in silico prediction tools.

New psychoactive substances (NPS) pose an increasing challenge for clinical and forensic toxicology due to the initial lack of analytical and metaboli...

Machine learning applications related to suicide in military and Veterans: A scoping literature review.

OBJECTIVE: Suicide remains one of the main preventable causes of death among service members and veterans. Early detection and accurate prediction are...

Machine learning-based prediction of anxiety disorders using blood metabolite and social trait data from the UK Biobank.

Anxiety disorders are the most prevalent type of mental health disorders and are characterised by excessive fear and worry. Despite affecting one in f...

Evaluating natural language processing derived linguistic features associated with current suicidal ideation, past attempts, and future suicidal behavior.

BACKGROUND: People with psychosis have a higher suicide risk than the general population. Natural language processing (NLP) has been used to understan...

Machine learning approaches for classifying major depressive disorder using biological and neuropsychological markers: A meta-analysis.

Traditional diagnostic methods for major depressive disorder (MDD), which rely on subjective assessments, may compromise diagnostic accuracy. In contr...

Prediction of post stroke depression with machine learning: A national multicenter cohort study.

OBJECTIVE: Post-stroke depression (PSD) is a common psychiatric complication following stroke, with low clinical detection rates and delayed diagnosis...

Brain Fractal Dimension and Machine Learning can predict first-episode psychosis and risk for transition to psychosis.

Although there are notable structural abnormalities in the brain associated with psychotic diseases, it is still unclear how these abnormalities relat...

Artificial intelligence in forensic mental health: A review of applications and implications.

This narrative review explores the transformative role of artificial intelligence (AI) in forensic mental health, focusing on its applications, benefi...

Machine learning approaches for fine-grained symptom estimation in schizophrenia: A comprehensive review.

Schizophrenia is a severe yet treatable mental disorder, and it is diagnosed using a multitude of primary and secondary symptoms. Diagnosis and treatm...

Self-training EEG discrimination model with weakly supervised sample construction: An age-based perspective on ASD evaluation.

Deep learning for Electroencephalography (EEG) has become dominant in the tasks of discrimination and evaluation of brain disorders. However, despite ...

Construction and verification of risk prediction model for suicidal attempts of mood disorder based on machine learning.

BACKGROUND: Mood disorders (MD) are closely related to suicide attempt (SA). Developing an effective prediction model for SA in MD patients could play...

Exploring artificial intelligence (AI) Chatbot usage behaviors and their association with mental health outcomes in Chinese university students.

Technology dependence has long been a critical public health issue, especially among young people. With the development of AI chatbots, many individua...

Anxiety disorder identification with biomarker detection through subspace-enhanced hypergraph neural network.

In this study, we propose a subspace-enhanced hypergraph neural network (seHGNN) for classifying anxiety disorders (AD), which are prevalent mental il...