AIMC Topic: Mood Disorders

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

Biological psychiatry Dec 10, 2019
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...
United States Gray Matter Mood Disorders Humans National Institute of Mental Health (U.S.) Machine Learning Brain
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Decoding rumination: A machine learning approach to a transdiagnostic sample of outpatients with anxiety, mood and psychotic disorders.

Journal of psychiatric research Dec 7, 2019
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.
Cytokines Female Adult Male Humans Models, Theoretical Support Vector Machine Psychotic Disorders Mood Disorders Middle Aged Anxiety Disorders Severity of Illness Index Social Class Adaptation, Psychological Rumination, Cognitive
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Personalized prediction of smartphone-based psychotherapeutic micro-intervention success using machine learning.

Journal of affective disorders Nov 14, 2019
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...
Affect Mood Disorders Humans Machine Learning Surveys and Questionnaires Smartphone
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Towards integrating personalized feedback research into clinical practice: Development of the Trier Treatment Navigator (TTN).

Behaviour research and therapy Jul 8, 2019
In this study, a computer-based feedback, decision and clinical problem-solving system for clinical practice will be described - the Trier Treatment Navigator (TTN). The paper deals with the underlying research concepts related to personalized pre-tr...
Adjustment Disorders Patient Dropouts Personality Disorders Decision Support Systems, Clinical Mood Disorders Problem Solving Cognitive Behavioral Therapy Surveys and Questionnaires Adolescent Stress Disorders, Post-Traumatic Female Aged Young Adult Adult Feedback Male Middle Aged Anxiety Disorders Machine Learning Humans Mental Disorders Precision Medicine Clinical Decision-Making
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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 Apr 17, 2019
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 ...
Humans Machine Learning Phenotype Prospective Studies Female Circadian Rhythm Male Mood Disorders Cohort Studies
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Meaningless comparisons lead to false optimism in medical machine learning.

PloS one Sep 26, 2017
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 ...
Humans Machine Learning Algorithms Mood Disorders
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Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective.

Progress in neuro-psychopharmacology & biological psychiatry Jun 23, 2017
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 ...
Machine Learning Brain Humans Magnetic Resonance Imaging Mood Disorders
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Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach.

Journal of affective disorders Jan 1, 2016
OBJECTIVE: A growing body of evidence has put forward clinical risk factors associated with patients with mood disorders that attempt suicide. However, what is not known is how to integrate clinical variables into a clinically useful tool in order to...
Middle Aged Humans Risk Assessment Machine Learning Comorbidity Female Pilot Projects Models, Statistical Young Adult Algorithms Suicide, Attempted Reproducibility of Results Adolescent Mood Disorders Adult Risk Factors Suicide Prevention Male Aged
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Construction and verification of risk prediction model for suicidal attempts of mood disorder based on machine learning.

Journal of affective disorders Jul 1, 2025
BACKGROUND: Mood disorders (MD) are closely related to suicide attempt (SA). Developing an effective prediction model for SA in MD patients could play a crucial role in the early identification of high-risk groups.
Mood Disorders Risk Factors Young Adult Adult Male Humans Middle Aged Machine Learning Risk Assessment Algorithms Suicide, Attempted Female
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Personalized prediction of negative affect in individuals with serious mental illness followed using long-term multimodal mobile phenotyping.

Translational psychiatry May 19, 2025
Heightened negative affect is a core feature of serious mental illness. Over 90% of American adults own a smartphone, equipped with an array of sensors which can continuously and unobtrusively measure behaviors (e.g., activity levels, location, and p...
Phenotype Female Young Adult Adult Psychotic Disorders Humans Affect Machine Learning Male Mood Disorders Middle Aged Accelerometry Wearable Electronic Devices Smartphone
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