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Mood Disorders

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Multiple Holdouts With Stability: Improving the Generalizability of Machine Learning Analyses of Brain-Behavior Relationships.

Biological psychiatry
BACKGROUND: In 2009, the National Institute of Mental Health launched the Research Domain Criteria, an attempt to move beyond diagnostic categories and ground psychiatry within neurobiological constructs that combine different levels of measures (e.g...

Meaningless comparisons lead to false optimism in medical machine learning.

PloS one
A new trend in medicine is the use of algorithms to analyze big datasets, e.g. using everything your phone measures about you for diagnostics or monitoring. However, these algorithms are commonly compared against weak baselines, which may contribute ...

Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective.

Progress in neuro-psychopharmacology & biological psychiatry
Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood ...

Understanding Mood Disorders in Children.

Advances in experimental medicine and biology
Mood disorders include all types of depression and bipolar disorder, and mood disorders are sometimes called affective disorders. We will discuss newly developing two issues in affective disorders in children and adolescents. Those are the new diagno...

Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study.

Journal of medical Internet research
BACKGROUND: Virtually, all organisms on Earth have their own circadian rhythm, and humans are no exception. Circadian rhythms are associated with various human states, especially mood disorders, and disturbance of the circadian rhythm is known to be ...

Personalized prediction of smartphone-based psychotherapeutic micro-intervention success using machine learning.

Journal of affective disorders
BACKGROUND: Tailoring healthcare to patients' individual needs is a central goal of precision medicine. Combining smartphone-based interventions with machine learning approaches may help attaining this goal. The aim of our study was to explore the pr...

Decoding rumination: A machine learning approach to a transdiagnostic sample of outpatients with anxiety, mood and psychotic disorders.

Journal of psychiatric research
OBJECTIVE: To employ machine learning algorithms to examine patterns of rumination from RDoC perspective and to determine which variables predict high levels of maladaptive rumination across a transdiagnostic sample.

Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample.

BMC psychiatry
BACKGROUND: This study aimed to determine conditional dependence relationships of variables that contribute to psychological vulnerability associated with suicide risk. A Bayesian network (BN) was developed and applied to establish conditional depend...

Relationships between motor and cognitive functions and subsequent post-stroke mood disorders revealed by machine learning analysis.

Scientific reports
Mood disorders (e.g. depression, apathy, and anxiety) are often observed in stroke patients, exhibiting a negative impact on functional recovery associated with various physical disorders and cognitive dysfunction. Consequently, post-stroke symptoms ...