Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom

EE Lee, J Torous, M De Choudhury, CA Depp… - Biological Psychiatry …, 2021 - Elsevier
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 …

[HTML][HTML] The role of machine learning in diagnosing bipolar disorder: sco** review

Z Jan, N Ai-Ansari, O Mousa, A Abd-Alrazaq… - Journal of medical …, 2021 - jmir.org
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 …

Neurobiology of bipolar disorders: a review of genetic components, signaling pathways, biochemical changes, and neuroimaging findings

G Scaini, SS Valvassori, AP Diaz, CN Lima… - Brazilian Journal of …, 2020 - SciELO Brasil
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 …

The use of machine learning techniques in trauma-related disorders: a systematic review

LF Ramos-Lima, V Waikamp… - Journal of psychiatric …, 2020 - Elsevier
Establishing the diagnosis of trauma-related disorders such as Acute Stress Disorder (ASD)
and Posttraumatic Stress Disorder (PTSD) have always been a challenge in clinical practice …

What we learn about bipolar disorder from large‐scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group

CRK Ching, DP Hibar, TP Gurholt, A Nunes… - Human brain …, 2022 - Wiley Online Library
MRI‐derived brain measures offer a link between genes, the environment and behavior and
have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of …

A Review of Implementing AI-Powered Data Warehouse Solutions to Optimize Big Data Management and Utilization

MKS Uddin, KMR Hossan - Academic Journal on Business …, 2024 - papers.ssrn.com
This review examines the implementation of AI-powered data warehouse solutions to
optimize big data management and utilization, analyzing 25 peer-reviewed articles …

Distinguishing between depression in bipolar disorder and unipolar depression using magnetic resonance imaging: a systematic review

JE Siegel‐Ramsay, MA Bertocci, B Wu… - Bipolar …, 2022 - Wiley Online Library
Objectives Magnetic resonance imaging (MRI) studies comparing bipolar and unipolar
depression characterize pathophysiological differences between these conditions. However …

[HTML][HTML] An overview of bipolar disorder diagnosis using machine learning approaches: clinical opportunities and challenges

WA Campos-Ugaz, JPP Garay… - Iranian Journal of …, 2023 - ncbi.nlm.nih.gov
Objective: Automatic diagnosis of psychiatric disorders such as bipolar disorder (BD)
through machine learning techniques has attracted substantial attention from psychiatric and …

Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: A systematic review and meta-analysis

F Colombo, F Calesella, MG Mazza… - Neuroscience & …, 2022 - Elsevier
Applying machine learning (ML) to objective markers may overcome prognosis uncertainty
due to the subjective nature of the diagnosis of bipolar disorder (BD). This PRISMA …

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

LA Claude, J Houenou, E Duchesnay… - Bipolar …, 2020 - Wiley Online Library
Objectives The existence of anatomofunctional brain abnormalities in bipolar disorder (BD)
is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic …