An insight into diagnosis of depression using machine learning techniques: a systematic review
Background In this modern era, depression is one of the most prevalent mental disorders
from which millions of individuals are affected today. The symptoms of depression are …
from which millions of individuals are affected today. The symptoms of depression are …
Classification of suicidality by training supervised machine learning models with brain MRI findings: A systematic review
Background Suicide is a global public health issue causing around 700,000 deaths
worldwide each year. Therefore, identifying suicidal thoughts and behaviors in patients can …
worldwide each year. Therefore, identifying suicidal thoughts and behaviors in patients can …
Application of artificial intelligence in the MRI classification task of human brain neurological and psychiatric diseases: a scoping review
Z Zhang, G Li, Y Xu, X Tang - Diagnostics, 2021 - mdpi.com
Artificial intelligence (AI) for medical imaging is a technology with great potential. An in-
depth understanding of the principles and applications of magnetic resonance imaging …
depth understanding of the principles and applications of magnetic resonance imaging …
Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome
Z Yang, L Jian, H Qiu, C Zhang, S Cheng, J Ji… - Translational …, 2021 - nature.com
Brain function relies on efficient communications between distinct brain systems. The
pathology of major depressive disorder (MDD) damages functional brain networks, resulting …
pathology of major depressive disorder (MDD) damages functional brain networks, resulting …
Artificial Intelligence-based Suicide Prevention and Prediction: A Systematic Review (2019-2023)
Suicide is a major global public health concern, and the application of artificial intelligence
(AI) methods, such as natural language processing (NLP), machine learning (ML), and deep …
(AI) methods, such as natural language processing (NLP), machine learning (ML), and deep …
[HTML][HTML] Polygonum sibiricum polysaccharides alleviate chronic unpredictable mild stress-induced depressive-like behaviors by regulating the gut microbiota …
Y Zhang, Y Sun, Y Liu, J Liu, J Sun, Y Bai, B Fan… - Journal of Functional …, 2023 - Elsevier
Polygonum sibiricum polysaccharides (PSP) are one of the main active components of
Polygonatum sibiricum. The present study was performed to deeply explore the …
Polygonatum sibiricum. The present study was performed to deeply explore the …
Childhood trauma and social support affect symptom profiles through cortical thickness abnormalities in major depressive disorder: a structural equation modeling …
C Jiang, W Jiang, G Chen, W Xu, T Sun, L You… - Asian Journal of …, 2023 - Elsevier
Background Childhood trauma, low social support, and alexithymia are recognized as risk
factors for major depressive disorder (MDD). However, the mechanisms of risk factors …
factors for major depressive disorder (MDD). However, the mechanisms of risk factors …
Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning
M Xu, X Zhang, Y Li, S Chen, Y Zhang, Z Zhou… - Translational …, 2022 - nature.com
Major depressive disorder (MDD) is a severe brain disease associated with a significant risk
of suicide. Identification of suicidality is sometimes life-saving for MDD patients. We aimed to …
of suicide. Identification of suicidality is sometimes life-saving for MDD patients. We aimed to …
Identifying suicide attempts, ideation, and non-ideation in major depressive disorder from structural MRI data using deep learning
The present study aims to identify suicide risks in major depressive disorders (MDD) patients
from structural MRI (sMRI) data using deep learning. In this paper, we collected the sMRI …
from structural MRI (sMRI) data using deep learning. In this paper, we collected the sMRI …
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 …