Psychiatry

Leveraging Embedding Techniques in Multimodal Machine Learning for Mental Illness Assessment

The increasing global prevalence of mental disorders, such as depression and PTSD, requires objective and scalable diagnostic tools. Traditional clinical assessments often face limitations in accessibility, objectivity, and consistency. This paper investigates the potential of multimodal machine learning to address these challenges, leveraging the complementary information available in text, aud...

Neural dynamics of mental state attribution to social robot faces.

The interplay of mind attribution and emotional responses is considered crucial in shaping human trust and acceptance of social robots. Understanding this interplay can help us create the right conditions for successful human-robot social interaction in alignment with societal goals. Our study shows that affective information about robots describing positive, negative, or neutral behaviour leads p...

Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models

Post-Traumatic Stress Disorder (PTSD) remains underdiagnosed in clinical settings, presenting opportunities for automated detection to identify pati...

Integrating Artificial Intelligence (AI) With Workforce Solutions for Sustainable Care: A Follow Up to Artificial Intelligence and Machine Learning (ML) Based Decision Support Systems in Mental Health.

This integrative literature review examines the evolving role of artificial intelligence (AI) and machine learning (ML) based clinical decision suppor...

A Framework for Comparison and Interpretation of Machine Learning Classifiers to Predict Autism on the ABIDE Dataset.

Autism is a neurodevelopmental condition affecting ~1% of the population. Recently, machine learning models have been trained to classify participants...

Generative AI and the profession of genetic counseling.

The development of artificial intelligence (AI) including generative large language models (LLMs) and software like ChatGPT is likely to significantly...

Using Machine Learning to Predict Uptake to an Online Self-Guided Intervention for Stress During the COVID-19 Pandemic.

Online self-guided interventions appear efficacious for alleviating some mental health concerns. However, among persons who are offered online interve...

Spatiotemporal Learning of Brain Dynamics from fMRI Using Frequency-Specific Multi-Band Attention for Cognitive and Psychiatric Applications

Understanding how the brain's complex nonlinear dynamics give rise to adaptive cognition and behavior is a central challenge in neuroscience. These ...

NeuroLIP: Interpretable and Fair Cross-Modal Alignment of fMRI and Phenotypic Text

Integrating functional magnetic resonance imaging (fMRI) connectivity data with phenotypic textual descriptors (e.g., disease label, demographic dat...

AutoPsyC: Automatic Recognition of Psychodynamic Conflicts from Semi-structured Interviews with Large Language Models

Psychodynamic conflicts are persistent, often unconscious themes that shape a person's behaviour and experiences. Accurate diagnosis of psychodynami...

Datasets for Depression Modeling in Social Media: An Overview

Depression is the most common mental health disorder, and its prevalence increased during the COVID-19 pandemic. As one of the most extensively rese...

Pupillary reactions depend on disgust sensitivity in conceptual pavlovian disgust conditioning

Exposure-based interventions rely on inhibitory learning, often studied through Pavlovian conditioning. While disgust conditioning is increasingly l...

Enhancing Depression Detection via Question-wise Modality Fusion

Depression is a highly prevalent and disabling condition that incurs substantial personal and societal costs. Current depression diagnosis involves ...

Bigger But Not Better: Small Neural Language Models Outperform Large Language Models in Detection of Thought Disorder

Disorganized thinking is a key diagnostic indicator of schizophrenia-spectrum disorders. Recently, clinical estimates of the severity of disorganize...

Peer Disambiguation in Self-Reported Surveys using Graph Attention Networks

Studying peer relationships is crucial in solving complex challenges underserved communities face and designing interventions. The effectiveness of ...

A Systematic Review of EEG-based Machine Intelligence Algorithms for Depression Diagnosis, and Monitoring

Depression disorder is a serious health condition that has affected the lives of millions of people around the world. Diagnosis of depression is a c...

Enabling Rapid Shared Human-AI Mental Model Alignment via the After-Action Review

In this work, we present two novel contributions toward improving research in human-machine teaming (HMT): 1) a Minecraft testbed to accelerate test...

FedSKD: Aggregation-free Model-heterogeneous Federated Learning using Multi-dimensional Similarity Knowledge Distillation

Federated learning (FL) enables privacy-preserving collaborative model training without direct data sharing. Model-heterogeneous FL (MHFL) extends t...

AI-Based Screening for Depression and Social Anxiety Through Eye Tracking: An Exploratory Study

Well-being is a dynamic construct that evolves over time and fluctuates within individuals, presenting challenges for accurate quantification. Reduc...

A Foundation Model for Patient Behavior Monitoring and Suicide Detection

Foundation models (FMs) have achieved remarkable success across various domains, yet their adoption in healthcare remains limited. While significant...