A novel depression risk prediction model based on data fusion from Chilean National Health Surveys to diagnose risk depression among patients with mood disorders
MF Guiñazú, M González, RB Ruiz, V Hernández… - Information …, 2023 - Elsevier
Artificial intelligence (AI)-based techniques have been widely applied in depression
research and treatment. Nevertheless, no specific predictor model for depression has been …
research and treatment. Nevertheless, no specific predictor model for depression has been …
[HTML][HTML] Predicting acute suicidal ideation on Instagram using ensemble machine learning models
Introduction Online social networking data (SN) is a contextually and temporally rich data
stream that has shown promise in the prediction of suicidal thought and behavior. Despite …
stream that has shown promise in the prediction of suicidal thought and behavior. Despite …
Predictive biosignature of major depressive disorder derived from physiological measurements of outpatients using machine learning
N Ricka, G Pellegrin, DA Fompeyrine, B Lahutte… - Scientific Reports, 2023 - nature.com
Abstract Major Depressive Disorder (MDD) has heterogeneous manifestations, leading to
difficulties in predicting the evolution of the disease and in patient's follow-up. We aimed to …
difficulties in predicting the evolution of the disease and in patient's follow-up. We aimed to …
Neuroimaging study of brain functional differences in generalized anxiety disorder and depressive disorder
X Qi, W Xu, G Li - Brain Sciences, 2023 - mdpi.com
Generalized anxiety disorder (GAD) and depressive disorder (DD) are distinct mental
disorders, which are characterized by complex and unique neuroelectrophysiological …
disorders, which are characterized by complex and unique neuroelectrophysiological …
Explaining models of mental health via clinically grounded auxiliary tasks
Abstract Models of mental health based on natural language processing can uncover latent
signals of mental health from language. Models that indicate whether an individual is …
signals of mental health from language. Models that indicate whether an individual is …
Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively …
Abstract Major Depressive Disorder (MDD) presents considerable challenges to diagnosis
and management due to symptom variability across time. Only recent work has highlighted …
and management due to symptom variability across time. Only recent work has highlighted …
[HTML][HTML] Machine learning–based predictive modeling of anxiety and depressive symptoms during 8 months of the COVID-19 global pandemic: Repeated cross …
Background: The COVID-19 global pandemic has increased the burden of mental illness on
Canadian adults. However, the complex combination of demographic, economic, and …
Canadian adults. However, the complex combination of demographic, economic, and …
Machine learning in E-health: a comprehensive survey of anxiety
Anxiety is the sixth major disorder that arises due to sleeplessness, inspiration, energy,
hunger loss and desperate ideas. This study aims to define the extent of the research carried …
hunger loss and desperate ideas. This study aims to define the extent of the research carried …
A novel smart belt for anxiety detection, classification, and reduction using IIoMT on students' cardiac signal and MSY
The prevalence of anxiety among university students is increasing, resulting in the negative
impact on their academic and social (behavioral and emotional) development. In order for …
impact on their academic and social (behavioral and emotional) development. In order for …
Enhancing the efficacy of depression detection system using optimal feature selection from EHR
Diagnosing depression at an early stage is crucial and majorly depends on the clinician's
skill. The present work aims to develop an automated tool for assisting the diagnostic …
skill. The present work aims to develop an automated tool for assisting the diagnostic …