Bipolar Disorder

Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics.

Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique, we aimed to formulate a model predicting antipsychotic treatment outcomes. Data were obtained from 299 patients with schizophrenia from three multicenter, open-label, non-comparative clinical trials. For prediction of treatment response at weeks 4,...

LSTM-based estimation of lithium-ion battery SOH using data characteristics and spatio-temporal attention.

As the primary power source for electric vehicles, the accurate estimation of the State of Health (SOH) of lithium-ion batteries is crucial for ensuring the reliable operation of the power system. Long Short-Term Memory (LSTM), a special type of recurrent neural network, achieves sequence information estimation through a gating mechanism. However, traditional LSTM-based SOH estimation methods do n...

Diagnosis of major depressive disorder using a novel interpretable GCN model based on resting state fMRI.

The diagnosis and analysis of major depressive disorder (MDD) faces some intractable challenges such as dataset limitations and clinical variability. ...

Momentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach.

BACKGROUND: Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as ...

Zinc or/and Vitamin E Supplementation Mitigates Oxidative Stress, Neuroinflammation, Neurochemical Changes and Behavioural Deficits in Male Wistar Rats Exposed to Bonny Light Crude Oil.

Crude oil, a major key economic driver in developing countries, is also of environmental concern, linked to neurotoxicity and behavioural problems. D...

Immune-based Machine learning Prediction of Diagnosis and Illness State in schizophrenia and bipolar Disorder: How data bias and overfitting were avoided.

In a letter critiquing our manuscript, Takefuji highlights general pitfalls in machine learning, without directly engaging with our study. The comment...

A novel ECG-based approach for classifying psychiatric disorders: Leveraging wavelet scattering networks.

Individuals with neuropsychiatric disorders experience both physical and mental difficulties, hindering their ability to live healthy lives and partic...

Model-informed approach to estimate treatment effect in placebo-controlled clinical trials using an artificial intelligence-based propensity weighting methodology to account for non-specific responses to treatment.

In randomized, placebo controlled clinical trials (RCT) in major depressive disorders (MDD), treatment response (TR) is estimated by the change from b...

RNA Editing Signatures Powered by Artificial Intelligence: A New Frontier in Differentiating Schizophrenia, Bipolar, and Schizoaffective Disorders.

Mental health disorders are devastating illnesses, often misdiagnosed due to overlapping clinical symptoms. Among these conditions, bipolar disorder, ...

Using machine learning to predict the probability of incident 2-year depression in older adults with chronic diseases: a retrospective cohort study.

BACKGROUND: Older adults with chronic diseases are at higher risk of depressive symptoms than those without. For the onset of depressive symptoms, the...

Reevaluating feature importances in machine learning models for schizophrenia and bipolar disorder: The need for true associations.

Skorobogatov et al. developed supervised machine learning models to predict diagnoses and illness states in schizophrenia and bipolar disorder. Howeve...

The efficacy of topological properties of functional brain networks in identifying major depressive disorder.

Major Depressive Disorder (MDD) is a common mental disorder characterized by cognitive impairment, and its pathophysiology remains to be explored. In ...

Altered Blood Oxygen Level-Dependent Signal Stability in the Brain of Patients with Major Depressive Disorder Undergoing Resting-State Functional Magnetic Resonance Imaging.

INTRODUCTION: Major depressive disorder (MDD) is a common, relapse-prone psychiatric disorder with unknown pathogenesis. Previous studies on resting-s...

The help-seeking process and predictors of mental health care use among individuals with depressive symptoms: a machine learning approach.

PURPOSE: The goal of the study was to identify the most important influences on professional healthcare use of people with depressive symptoms. We inc...

Development of machine learning models for predicting depressive symptoms in knee osteoarthritis patients.

Knee osteoarthritis (KOA) combined with depressive symptoms is prevalent and leads to poor outcomes and significant financial burdens. However, practi...

AI-Aided Gait Analysis with a Wearable Device Featuring a Hydrogel Sensor.

Wearable devices have revolutionized real-time health monitoring, yet challenges persist in enhancing their flexibility, weight, and accuracy. This pa...

Development and Validation of a Prediction Model for Co-Occurring Moderate-to-Severe Anxiety Symptoms in First-Episode and Drug Naïve Patients With Major Depressive Disorder.

Moderate-to-severe anxiety symptoms are severe and common in patients with major depressive disorder (MDD) and have a significant impact on MDD patie...

An adaptive multi-graph neural network with multimodal feature fusion learning for MDD detection.

Major Depressive Disorder (MDD) is an affective disorder that can lead to persistent sadness and a decline in the quality of life, increasing the risk...

Bipolar Fuzzy Pseudo-UP Ideal Of Pseudo-UP Algebra.

In this paper, we apply the concept of bipolar fuzzy sets to pseudo-UP ideals in pseudo-UP algebras. We prove that the intersection of two bipolar fuz...

The voice of depression: speech features as biomarkers for major depressive disorder.

BACKGROUND: Psychiatry faces a challenge due to the lack of objective biomarkers, as current assessments are based on subjective evaluations. Automate...