AIMC Topic: Bipolar Disorder
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Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study.
PloS one
Dec 31, 2020
BACKGROUND: A priority for health services is to reduce self-harm in young people. Predicting self-harm is challenging due to their rarity and complexity, however this does not preclude the utility of prediction models to improve decision-making rega...
Comparing machine and deep learning-based algorithms for prediction of clinical improvement in psychosis with functional magnetic resonance imaging.
Human brain mapping
Nov 13, 2020
Previous work using logistic regression suggests that cognitive control-related frontoparietal activation in early psychosis can predict symptomatic improvement after 1 year of coordinated specialty care with 66% accuracy. Here, we evaluated the abil...
Deep Learning
Functional Neuroimaging
Adolescent
Bipolar Disorder
Humans
Female
Outcome Assessment, Health Care
Machine Learning
Young Adult
Support Vector Machine
Psychomotor Performance
Adult
Follow-Up Studies
Dorsolateral Prefrontal Cortex
Male
Magnetic Resonance Imaging
Executive Function
Schizophrenia
Big data in severe mental illness: the role of electronic monitoring tools and metabolomics.
Personalized medicine
Oct 30, 2020
There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized ...
A peripheral inflammatory signature discriminates bipolar from unipolar depression: A machine learning approach.
Progress in neuro-psychopharmacology & biological psychiatry
Oct 9, 2020
BACKGROUND: Mood disorders (major depressive disorder, MDD, and bipolar disorder, BD) are considered leading causes of life-long disability worldwide, where high rates of no response to treatment or relapse and delays in receiving a proper diagnosis ...
Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning.
Molecular psychiatry
Oct 1, 2020
Converging evidence increasingly implicates shared etiologic and pathophysiological characteristics among major psychiatric disorders (MPDs), such as schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). Examining the neurob...
A deep learning model for detecting mental illness from user content on social media.
Scientific reports
Jul 16, 2020
Users of social media often share their feelings or emotional states through their posts. In this study, we developed a deep learning model to identify a user's mental state based on his/her posting information. To this end, we collected posts from m...
Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action.
European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
Jun 12, 2020
Bipolar disorder (BD) is a major healthcare and socio-economic challenge. Despite its substantial burden on society, the research activity in BD is much smaller than its economic impact appears to demand. There is a consensus that the accurate identi...
Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling.
International journal of medical informatics
Mar 31, 2020
BACKGROUND: Bipolar disorder (BD) is a chronic illness with a high recurrence rate. Smartphones can be a useful tool for detecting prodromal symptoms of episode recurrence (through real-time monitoring) and providing options for early intervention be...
Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions.
Bipolar disorders
Mar 20, 2020
OBJECTIVES: The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subt...
A proof of concept machine learning analysis using multimodal neuroimaging and neurocognitive measures as predictive biomarker in bipolar disorder.
Asian journal of psychiatry
Feb 26, 2020
BACKGROUND: Concomitant use of complementary, multimodal imaging measures and neurocognitive measures is reported to have higher accuracy as a biomarker in Alzheimer's dementia. However, such an approach has not been examined to differentiate healthy...
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