Depression

Entropy-Dominated Temporal Vocal Dynamics as Digital Biomarkers for Depression Detection

Automated depression detection often relies on static aggregation of conversational signals, potentially obscuring clinically meaningful behavioral dynamics. We investigated whether entropy-driven temporal biomarkers improve depression detection beyond standard pooled features using the DAIC-WOZ corpus. Using 142 labeled participants, we reconstructed utterance-level acoustic trajectories and comp...

Psychologically-Grounded Graph Modeling for Interpretable Depression Detection

Automatic depression detection from conversational interactions holds significant promise for scalable screening but remains hindered by severe data scarcity and a lack of clinical interpretability. Existing approaches typically rely on black-box deep learning architectures that struggle to model the subtle, temporal evolution of depressive symptoms or account for participant-specific heterogeneit...

FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment

In recent years, the integration of multimodal machine learning in wellbeing assessment has offered transformative potential for monitoring mental hea...

Predicting Depressive Symptoms Among Reproductive-Aged Women in Bangladesh Using Bagging Ensemble Machine Learning on Imbalanced Bangladesh Demographic and Health Survey 2022 Data

Background Depressive symptoms among reproductive-aged women represent a major public health concern in low- and middle-income countries, yet systemat...

Modeling Higher-Order Brain Interactions via a Multi-View Information Bottleneck Framework for fMRI-based Psychiatric Diagnosis

Resting-state functional magnetic resonance imaging (fMRI) has emerged as a cornerstone for psychiatric diagnosis, yet most approaches rely on pairwis...

The Euler Characteristic Transform Enables Classification of Complex Plant Shapes and Prediction of Leaf Venation from Blade Geometry

Rationale: Quantifying and predicting plant morphology is central to understanding development and evolution, yet many plant forms lack homologous fea...

Developing a Tiered Machine Learning Alert System for Real-Time Suicide Risk Detection in a Digital Mental Health Setting

The goal of this work was to leverage a large corpus of text based psychotherapy data to create novel machine learning algorithms that can identify su...

MAMGL: A memory-augmented meta-graph learning framework for adolescent major depression disorder diagnosis

Adolescent major depressive disorder (AMDD) is a prevalent and heterogeneous psychiatric condition that emerges during a critical period of brain deve...

Identification of Suicide-Related Subgroups Using Latent Class Analysis: Complementary Insights to Explainable AI-Based Classification

Purpose: Suicide and self-harm are major public health concerns characterized by substantial clinical and psychosocial heterogeneity. While latent cla...

Aggregate benchmark scores obscure patient safety implications of errors across frontier language models

Frontier language models are widely used for health-related queries, yet aggregate benchmark scores do not capture safety implications of errors. We a...

Bayesian Inference of Psychometric Variables From Brain and Behavior in Implicit Association Tests

Objective. We establish a principled method for inferring mental health related psychometric variables from neural and behavioral data using the Impli...

A widespread electrical brain network encodes anxiety in health and depressive states

In rodents, anxiety is characterized by heightened vigilance during low-threat and uncertain situations. Though activity in the frontal cortex and lim...

Multi-Criteria Validation of LLM-Inferred Depression Severity from Outpatient Psychiatry Notes

Background: Longitudinal measurement of depression severity in outpatient psychiatric care is limited by infrequent standardized assessments. Although...

IDRL: An Individual-Aware Multimodal Depression-Related Representation Learning Framework for Depression Diagnosis

Depression is a severe mental disorder, and reliable identification plays a critical role in early intervention and treatment. Multimodal depression d...

Learning the Hierarchical Organization in Brain Network for Brain Disorder Diagnosis

Brain network analysis based on functional Magnetic Resonance Imaging (fMRI) is pivotal for diagnosing brain disorders. Existing approaches typically ...

BrainSTR: Spatio-Temporal Contrastive Learning for Interpretable Dynamic Brain Network Modeling

Dynamic functional connectivity captures time-varying brain states for better neuropsychiatric diagnosis and spatio-temporal interpretability, i.e., i...

Unsupervised Domain Adaptation with Target-Only Margin Disparity Discrepancy

In interventional radiology, Cone-Beam Computed Tomography (CBCT) is a helpful imaging modality that provides guidance to practicians during minimally...

Bias and Fairness in Self-Supervised Acoustic Representations for Cognitive Impairment Detection

Speech-based detection of cognitive impairment (CI) offers a promising non-invasive approach for early diagnosis, yet performance disparities across d...

Disentangling Symptom Heterogeneity in Large-Scale Psychiatric Text: Domain-Adapted vs. Instruction-Tuned Transformers

Psychiatric disorders are fundamentally challenged by symptom heterogeneity, high comorbidity, and the absence of objective biomarkers, which together...

Precision stratification of risk for suicidal behavior in people with bipolar depression

Patients with bipolar depression are at the highest risk for suicidal behavior, comprising ~10% of all deaths. In the critical period preceding attemp...