Psychiatry

Bipolar Disorder

Latest AI and machine learning research in bipolar disorder for healthcare professionals.

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From the -Factor to Cognitive Content: Detection and Discrimination of Psychopathologies Based on Explainable Artificial Intelligence.

Differentiating psychopathologies is challenging due to shared underlying mechanisms, such as the -...

A lithium-ion batteries SOH estimation method based on extracting new features during the constant voltage charging stage and improving BPNN.

Existing state of health (SOH) estimation methods for lithium-ion batteries predominantly extract he...

The Hypothalamic Medial Preoptic Area-Paraventricular Nucleus Circuit Modulates Depressive-Like Behaviors in a Mouse Model of Postpartum Depression.

Estrogen fluctuations have been implicated in various mood disorders, including perimenopausal and p...

Predicting Suicidal Ideation Among Youths With Autism Spectrum Disorder: An Advanced Machine Learning Study.

This study aimed to predict suicidal ideation among youth with autism spectrum disorder (ASD) by app...

Predicting depression severity using machine learning models: Insights from mitochondrial peptides and clinical factors.

Depression presents a significant challenge to global mental health, often intertwined with factors ...

A Clinical Risk Prediction Model for Depressive Disorders Based on Seven Machine Learning Algorithms.

OBJECTIVE: To develop a clinical risk prediction model for depressive disorders using seven machine ...

Semantic abnormalities in schizophrenia and bipolar disorder: A natural language processing approach.

INTRODUCTION: The diagnostic boundaries between schizophrenia and bipolar disorder are controversial...

Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group.

Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-...

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 ...

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 ...

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 ...

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 re...

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 disor...

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 a...

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...

[Application of NGO-BP Neural Network in Battery Life Prediction of Portable Medical Devices].

The development of portable medical devices cannot be separated from safe and efficient batteries. A...

The New Emerging Treatment Choice for Major Depressive Disorders: Digital Therapeutics.

The chapter provides an in-depth analysis of digital therapeutics (DTx) as a revolutionary approach ...

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