Bipolar Disorder

A machine learning model using clinical notes to estimate PHQ-9 symptom severity scores in depressed patients.

BACKGROUND: Lack of widespread use of the Patient Health Questionnaire 9-item (PHQ-9) in clinical practice inhibits measurement of treatment follow-up for patients with major depressive disorder (MDD). This study developed, validated and applied a machine learning model to estimate PHQ-9 scores for MDD patients using relevant notes from electronic medical records (EMR).

AFMDD: Analyzing Functional Connectivity Feature of Major Depressive Disorder by Graph Neural Network-Based Model.

The extraction of biomarkers from functional connectivity (FC) in the brain is of great significance for the diagnosis of mental disorders. In recent years, with the development of deep learning, several methods have been proposed to assist in the diagnosis of depression and promote its automatic identification. However, these methods still have some limitations. The current approaches overlook th...

Optimal selection of diagnostic method for diabetes mellitus using complex bipolar fuzzy dynamic data.

Diabetes mellitus refers to a collection of metabolic disorders that affect the way carbohydrates are processed in the body. It is a prominent worldwi...

Machine Learning Tool for New Selective Serotonin and Serotonin-Norepinephrine Reuptake Inhibitors.

Depression, a serious mood disorder, affects about 5% of the population. Currently, there are two groups of antidepressants that are the first-line tr...

Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis.

BACKGROUND: Diagnosing bipolar disorder poses a challenge in clinical practice and demands a substantial time investment. With the growing utilization...

Development and applications of a machine learning model for an in-depth analysis of pentylenetetrazol-induced seizure-like behaviors in adult zebrafish.

Epilepsy, a neurological disorder causing recurring seizures, is often studied in zebrafish by exposing animals to pentylenetetrazol (PTZ), which indu...

The integrating of environmental sustainability assessment by using bipolar complex fuzzy soft Aczel-Alsina aggregation operators with EDAS approach.

The act of responsibly engaging with the world is referred to as environmental sustainability. It entails preserving the world's ecosystems and natura...

Identification of depressive symptoms in adolescents using machine learning combining childhood and adolescence features.

BACKGROUND: Depressive symptoms in adolescents can significantly affect their daily lives and pose risks to their future development. These symptoms m...

Machine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder.

INTRODUCTION: Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinic...

Machine learning-based prediction of illness course in major depression: The relevance of risk factors.

BACKGROUND: Major depressive disorder (MDD) comes along with an increased risk of recurrence and poor course of illness. Machine learning has recently...

Natural language processing to identify suicidal ideation and anhedonia in major depressive disorder.

BACKGROUND: Anhedonia and suicidal ideation are symptoms of major depressive disorder (MDD) that are not regularly captured in structured scales but m...

Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.

Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its di...

Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder.

Depression treatment responses vary widely among individuals. Identifying objective biomarkers with predictive accuracy for therapeutic outcomes can e...

Is Artificial Intelligence the Next Co-Pilot for Primary Care in Diagnosing and Recommending Treatments for Depression?

Depression poses significant challenges to global healthcare systems and impacts the quality of life of individuals and their family members. Recent a...

Enhancing prediction of major depressive disorder onset in adolescents: A machine learning approach.

Major Depressive Disorder (MDD) is a prevalent mental health condition that often begins in adolescence, with significant long-term implications. Indi...

Automated classification of stress and relaxation responses in major depressive disorder, panic disorder, and healthy participants via heart rate variability.

BACKGROUND: Stress is a significant risk factor for psychiatric disorders such as major depressive disorder (MDD) and panic disorder (PD). This highli...

Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning.

Spontaneous blood oxygen level-dependent signals can be indirectly recorded in different brain regions with functional magnetic resonance imaging. In ...

Applied pharmacogenetics to predict response to treatment of first psychotic episode: study protocol.

The application of personalized medicine in patients with first-episode psychosis (FEP) requires tools for classifying patients according to their res...

DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.

In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parc...

AI-driven identification of a novel malate structure from recycled lithium-ion batteries.

The integration of Artificial Intelligence (AI) into the discovery of new materials offers significant potential for advancing sustainable technologie...