Depression

Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning-Based Advanced Perspectives.

Mental disorders are a representative type of brain disorder, including anxiety, major depressive depression (MDD), and autism spectrum disorder (ASD), that are caused by multiple etiologies, including genetic heterogeneity, epigenetic dysregulation, and aberrant morphological and biochemical conditions. Psychedelic drugs such as psilocybin and lysergic acid diethylamide (LSD) have been renewed as...

Protocol for socioecological study of autism, suicide risk, and mental health care: Integrating machine learning and community consultation for suicide prevention.

INTRODUCTION: Autistic people experience higher risk of suicidal ideation (SI) and suicide attempts (SA) compared to non-autistic people, yet there is limited understanding of complex, multilevel factors that drive this disparity. Further, determinants of mental health service receipt among this population are unknown. This study will identify socioecological factors associated with increased risk...

Machine learned text topics improve drop-out risk prediction but not symptom prediction in online psychotherapies for depression and anxiety.

Objective: Internet-delivered cognitive behavior therapies (iCBT) are effective and scalable treatments for depression and anxiety. However, treatment...

Utilizing machine learning algorithms for predicting Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI).

BACKGROUND: Accurately diagnosing Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI) shows significant challenges as tra...

Using machine learning to predict depression among middle-aged and elderly population in China and conducting empirical analysis.

OBJECTIVE: To develop a predictive model for evaluating depression among middle-aged and elderly individuals in China.

Utilizing machine learning to identify fall predictors in India's aging population: findings from the LASI.

BACKGROUND: Depression has a detrimental effect on an individual's mental and musculoskeletal strength multiplying the risk of fall incidents. The cur...

Heterogeneous Graph Representation Learning Framework for Resting-State Functional Connectivity Analysis.

Brain functional connectivity analysis is important for understanding brain development and brain disorders. Recent studies have suggested that the va...

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry.

The deployment of artificial intelligence (AI) is revolutionizing neuropharmacology and drug development, allowing the modulation of neurotransmitter ...

AI-based personalized real-time risk prediction for behavioral management in psychiatric wards using multimodal data.

BACKGROUND: Suicide is a major global health issue, with approximately 700,000 deaths annually (WHO). In psychiatric wards, managing harmful behaviors...

Neuronal and therapeutic perspectives on empathic pain: A rational insight.

Empathy is the capacity to experience and understand the feelings of others, thereby playing a key role in a person's mental well-being essentially by...

Altered resting-state brain activity in patients with major depression disorder and bipolar disorder: A regional homogeneity analysis.

BACKGROUND: Major Depressive Disorder (MDD) and Bipolar Disorder (BD) exhibit overlapping depressive symptoms, complicating their differentiation in c...

A multimodal approach to depression diagnosis: insights from machine learning algorithm development in primary care.

General practitioners play an essential role in identifying depression and are often the first point of contact for patients. Current diagnostic tools...

Continuous nursing symptom management in cancer chemotherapy patients using deep learning.

To assess the efficacy of a deep learning platform for managing symptoms in chemotherapy patients, aiming to enhance their quality of life. A non-rand...

Prediction of postpartum depression in women: development and validation of multiple machine learning models.

BACKGROUND: Postpartum depression (PPD) is a significant public health issue. This study aimed to develop and validate machine learning (ML) models us...

Research on prediction model of adolescent suicide and self-injury behavior based on machine learning algorithm.

OBJECTIVE: To explore the risk factors that affect adolescents' suicidal and self-injurious behaviors and to construct a prediction model for adolesce...

Preprocessing of natural language process variables using a data-driven method improves the association with suicide risk in a large veterans affairs population.

OBJECTIVE: Suicide risk assessment has historically relied heavily on clinical evaluations and patient self-reports. Natural language processing (NLP)...

A machine learning approach to predict treatment efficacy and adverse effects in major depression using CYP2C19 and clinical-environmental predictors.

BACKGROUND: Major depressive disorder (MDD) is among the leading causes of disability worldwide and treatment efficacy is variable across patients. Po...

Real-time monitoring to predict depressive symptoms: study protocol.

INTRODUCTION: According to the World Health Organization, Depression is the fourth leading cause of global disease burden. However, traditional clinic...

Circadian rhythm modulation in heart rate variability as potential biomarkers for major depressive disorder: A machine learning approach.

Major depressive disorder (MDD) is associated with reduced heart rate variability (HRV), but its link to circadian rhythm modulation (CRM) of HRV is u...

Bootstrap inference and machine learning reveal core differential plasma metabolic connectome signatures in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) involves molecular alterations and pathway dysregulation. Metabolic interconnections are crucial for norma...