Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom
Artificial intelligence (AI) is increasingly employed in health care fields such as oncology,
radiology, and dermatology. However, the use of AI in mental health care and …
radiology, and dermatology. However, the use of AI in mental health care and …
The impact of machine learning techniques in the study of bipolar disorder: a systematic review
D Librenza-Garcia, BJ Kotzian, J Yang… - Neuroscience & …, 2017 - Elsevier
Abstract Machine learning techniques provide new methods to predict diagnosis and clinical
outcomes at an individual level. We aim to review the existing literature on the use of …
outcomes at an individual level. We aim to review the existing literature on the use of …
Chinese college students have higher anxiety in new semester of online learning during COVID-19: a machine learning approach
C Wang, H Zhao, H Zhang - Frontiers in psychology, 2020 - frontiersin.org
The COVID-19 pandemic has caused tremendous loss starting from early this year. This
article aims to investigate the change of anxiety severity and prevalence among non …
article aims to investigate the change of anxiety severity and prevalence among non …
Staging in bipolar disorder: from theoretical framework to clinical utility
Illness staging is widely utilized in several medical disciplines to help predict course or
prognosis, and optimize treatment. Staging models in psychiatry in general, and bipolar …
prognosis, and optimize treatment. Staging models in psychiatry in general, and bipolar …
Hippocampal subfield volumes in mood disorders
Volume reduction and shape abnormality of the hippocampus have been associated with
mood disorders. However, the hippocampus is not a uniform structure and consists of …
mood disorders. However, the hippocampus is not a uniform structure and consists of …
[HTML][HTML] The role of machine learning in diagnosing bipolar disorder: sco** review
Background Bipolar disorder (BD) is the 10th most common cause of frailty in young
individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life …
individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life …
Using structural MRI to identify bipolar disorders–13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective
biological markers, such as those based on brain imaging, could aid in clinical management …
biological markers, such as those based on brain imaging, could aid in clinical management …
Neurobiology of bipolar disorders: a review of genetic components, signaling pathways, biochemical changes, and neuroimaging findings
Bipolar disorder (BD) is a chronic mental illness characterized by changes in mood that
alternate between mania and hypomania or between depression and mixed states, often …
alternate between mania and hypomania or between depression and mixed states, often …
Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry
Background The development of machine learning models for aiding in the diagnosis of
mental disorder is recognized as a significant breakthrough in the field of psychiatry …
mental disorder is recognized as a significant breakthrough in the field of psychiatry …
Lifespan gyrification trajectories of human brain in healthy individuals and patients with major psychiatric disorders
Cortical gyrification of the brain represents the folding characteristic of the cerebral cortex.
How the brain cortical gyrification changes from childhood to old age in healthy human …
How the brain cortical gyrification changes from childhood to old age in healthy human …