Depression

Psychosocial Factors and Psychological Characteristics of Personality of Patients with Chronic Diseases Using Artificial Intelligence Data Mining Technology and Wireless Network Cloud Service Platform.

It was to explore the application value of health cloud service platform based on data mining algorithm and wireless network in the analysis of psychosocial factors and psychological characteristics of personality of patients with chronic diseases. Based on the demand analysis of cloud service platform for chronic diseases, a health cloud service platform including three modules was established: s...

Large-Scale Textual Datasets and Deep Learning for the Prediction of Depressed Symptoms.

Millions of people worldwide suffer from depression. Assessing, treating, and preventing recurrence requires early detection of depressive symptoms as depression-related datasets expand and machine learning improves, intelligent approaches to detect depression in written material may emerge. This study provides an effective method for identifying texts describing self-perceived depressive symptoms...

Polysomnographic identification of anxiety and depression using deep learning.

Anxiety and depression are common psychiatric conditions associated with significant morbidity and healthcare costs. Sleep is an evolutionarily conser...

Missing data imputation in clinical trials using recurrent neural network facilitated by clustering and oversampling.

In clinical practice, the composition of missing data may be complex, for example, a mixture of missing at random (MAR) and missing not at random (MNA...

Design and Evaluation of a Postpartum Depression Ontology.

OBJECTIVE: Postpartum depression (PPD) remains an understudied research area despite its high prevalence. The goal of this study is to develop an onto...

Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning.

Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness...

Expressions of anger during advising on life dilemmas predict suicide risk among college students.

Research has demonstrated a relationship between anger and suicidality, while real-time authentic emotions behind facial expressions could be detected...

Mood State Detection in Handwritten Tasks Using PCA-mFCBF and Automated Machine Learning.

In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood s...

Automated detection of clinical depression based on convolution neural network model.

As a common mental disorder, depression is placing an increasing burden on families and society. However, the current methods of depression detection ...

Continuous Scoring of Depression From EEG Signals via a Hybrid of Convolutional Neural Networks.

Depression score is traditionally determined by taking the Beck depression inventory (BDI) test, which is a qualitative questionnaire. Quantitative sc...

Analysis of Clinical Parameters, Drug Consumption and Use of Health Resources in a Southern European Population with Alcohol Abuse Disorder during COVID-19 Pandemic.

The disruption in healthcare attention to people with alcohol dependence, along with psychological decompensation as a consequence of lockdown derived...

Income inequalities, social support and depressive symptoms among older adults in Europe: a multilevel cross-sectional study.

UNLABELLED: This study analysed the association between income inequality and depression from a multilevel perspective among older adults in Europe, i...

Detection of child depression using machine learning methods.

BACKGROUND: Mental health problems, such as depression in children have far-reaching negative effects on child, family and society as whole. It is nec...

Formulation and physicochemical stability of oil-in-water nanoemulsion loaded with α-terpineol as flavor oil using Quillaja saponins as natural emulsifier.

Alpha-terpineol (α-TOH) is a promising monoterpenoid detaining several biological activities. However, as a volatile molecule, the incorporation of α-...

Understanding Medical Distrust Among African American/Black and Latino Persons Living With HIV With Sub-Optimal Engagement Along the HIV Care Continuum: A Machine Learning Approach.

Medical distrust is a potent barrier to participation in HIV care and medication use among African American/Black and Latino (AABL) persons living wit...

Treatment selection using prototyping in latent-space with application to depression treatment.

Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of pat...

Mechanisms and Methods to Understand Depressive Symptoms.

Depressive symptoms, feelings of sadness, anger, and loss that interfere with a person's daily life, are prevalent health concerns across populations ...

Predicting suicidal thoughts and behavior among adolescents using the risk and protective factor framework: A large-scale machine learning approach.

INTRODUCTION: Addressing the problem of suicidal thoughts and behavior (STB) in adolescents requires understanding the associated risk factors. While ...

Using CatBoost algorithm to identify middle-aged and elderly depression, national health and nutrition examination survey 2011-2018.

Depression is one of the most common mental health problems in middle-aged and elderly people. The establishment of risk factor-based depression risk ...

Differential power of placebo across major psychiatric disorders: a preliminary meta-analysis and machine learning study.

The placebo effect across psychiatric disorders is still not well understood. In the present study, we conducted meta-analyses including meta-regressi...