Bipolar Disorder

Multi-atlas Ensemble Graph Neural Network Model For Major Depressive Disorder Detection Using Functional MRI Data

Major depressive disorder (MDD) is one of the most common mental disorders, with significant impacts on many daily activities and quality of life. It stands as one of the most common mental disorders globally and ranks as the second leading cause of disability. The current diagnostic approach for MDD primarily relies on clinical observations and patient-reported symptoms, overlooking the diverse...

Emotional Vietnamese Speech-Based Depression Diagnosis Using Dynamic Attention Mechanism

Major depressive disorder is a prevalent and serious mental health condition that negatively impacts your emotions, thoughts, actions, and overall perception of the world. It is complicated to determine whether a person is depressed due to the symptoms of depression not apparent. However, their voice can be one of the factor from which we can acknowledge signs of depression. People who are depre...

Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder

For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) h...

Forward and Inverse Simulation of Pseudo-Two-Dimensional Model of Lithium-Ion Batteries Using Neural Networks

In this work, we address the challenges posed by the high nonlinearity of the Butler-Volmer (BV) equation in forward and inverse simulations of the ...

Toward molecular diagnosis of major depressive disorder by plasma peptides using a deep learning approach.

Major depressive disorder (MDD) is a severe psychiatric disorder that currently lacks any objective diagnostic markers. Here, we develop a deep learni...

Cyborg Insect Factory: Automatic Assembly System to Build up Insect-computer Hybrid Robot Based on Vision-guided Robotic Arm Manipulation of Custom Bipolar Electrodes

The advancement of insect-computer hybrid robots holds significant promise for navigating complex terrains and enhancing robotics applications. This...

Making the most of errors: Utilizing erroneous classifications generated by machine-learning models of neuroimaging data to capture disorder heterogeneity.

Within-disorder heterogeneity complicates mapping the neurobiological features of psychopathology to Diagnostic and Statistical Manual of Mental Disor...

AI-Driven Early Mental Health Screening: Analyzing Selfies of Pregnant Women

Major Depressive Disorder and anxiety disorders affect millions globally, contributing significantly to the burden of mental health issues. Early sc...

Analysis of In-Home Movement Patterns for Depression Assessment in Older Adults - A Feasibility Study.

Depression significantly impacts the wellbeing of older Australians, posing considerable challenges to their overall quality of life. This study aimed...

Multilayer Network of Cardiovascular Diseases and Depression via Multipartite Projection

Cardiovascular diseases (CVD) and depression exhibit significant comorbidity, which is highly predictive of poor clinical outcomes. Yet, the underly...

Integrating Large Language Models into a Tri-Modal Architecture for Automated Depression Classification on the DAIC-WOZ

Major Depressive Disorder (MDD) is a pervasive mental health condition that affects 300 million people worldwide. This work presents a novel, BiLSTM...

Interpreting artificial neural networks to detect genome-wide association signals for complex traits

Investigating the genetic architecture of complex diseases is challenging due to the multifactorial and interactive landscape of genomic and environ...

Hybrid Temporal Computing for Lower Power Hardware Accelerators

In this paper, we propose a new hybrid temporal computing (HTC) framework that leverages both pulse rate and temporal data encoding to design ultra-...

MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal.

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-being and is often detected at a later stage of depression wit...

Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling.

MAIN PROBLEM: Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the n...

Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram Data.

While deep learning methods are increasingly applied in research contexts for neuropsychiatric disorder diagnosis, small dataset size limits their pot...

Exploring Self-Supervised Models for Depressive Disorder Detection: A Study on Speech Corpora.

Automatic detection of depressive disorder from speech signals can help improve medical diagnosis reliability. However, a significant challenge in thi...

Diagnosing Suicidal Ideation from Resting State EEG Data Using a Machine Learning Algorithm.

Suicide poses a global health crisis with significant social and economic impact. Prevention may be possible if objective quantitative methods are dev...

FacePsy: An Open-Source Affective Mobile Sensing System -- Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic Settings

Depression, a prevalent and complex mental health issue affecting millions worldwide, presents significant challenges for detection and monitoring. ...

Development and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study

INTRODUCTION: The pharmacological treatment of Major Depressive Disorder (MDD) relies on a trial-and-error approach. We introduce an artificial inte...