AIMC Topic: Depressive Disorder

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Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection.

BMC psychiatry
OBJECTIVE: To develop a stratified screening tool through machine learning approaches for the Center for Epidemiologic Studies Depression Scale (CES-D-20) while maintaining diagnostic accuracy, addressing the efficiency limitations in large-scale app...

An interpretable deep-learning approach to detect biomarkers in anxious-depressed symptoms from prefrontal fNIRS signals during an autobiographical memory test.

Asian journal of psychiatry
BACKGROUND: Individuals with anxious-depressed (AD) symptoms have more severe mood disorders and cognitive impairment than those with non-anxious depression (NAD) symptoms. Therefore, it is important to clarify the underlying neurophysiology of these...

Personalising Antidepressant Treatment for Unipolar Depression Combining Individual Choices, Risks and big Data: The PETRUSHKA Tool: Personnalisation du traitement antidépresseur de la dépression unipolaire associant choix individuels, risques et mégadonnées: l'outil PETRUSHKA.

Canadian journal of psychiatry. Revue canadienne de psychiatrie
OBJECTIVE: We summarize the key steps to develop and assess an innovative online, evidence-based tool that supports shared decision-making in routine care to personalize antidepressant treatment in adults with depression. This PETRUSHKA tool is part ...

Efficacy and effectiveness of therapist-guided internet versus face-to-face cognitive behavioural therapy for depression via counterfactual inference using naturalistic registers and machine learning in Finland: a retrospective cohort study.

The lancet. Psychiatry
BACKGROUND: According to meta-analyses of randomised controlled trials (RCTs), therapist-guided internet-delivered cognitive behavioural therapy (iCBT) is as effective a treatment for depression as traditional face-to-face CBT (fCBT), despite its sub...

Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study.

BMC psychiatry
OBJECTIVE: Depression has emerged as a global public health concern with high incidence and disability rates, which are timely imperative to identify and intervene in clinical practice. The objective of this study was to explore the association betwe...

Depression diagnosis: EEG-based cognitive biomarkers and machine learning.

Behavioural brain research
Depression is a complex mental illness that has significant effects on people as well as society. The traditional techniques for the diagnosis of depression, along with the potential of nascent biomarkers especially EEG-based biomarkers, are studied....

Machine learning-based discrimination of unipolar depression and bipolar disorder with streamlined shortlist in adolescents of different ages.

Computers in biology and medicine
BACKGROUND: Variations in symptoms and indistinguishable depression episodes of unipolar depression (UD) and bipolar disorder (BD) make the discrimination difficult and time-consuming. For adolescents with high disease prevalence, an efficient diagno...

Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review.

Journal of affective disorders
OBJECTIVES: This study underscores the importance of exploring AI's creative applications in treating depressive disorders to revolutionize mental health care. Through innovative integration of AI technologies, the research confirms their positive ef...

Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model.

Scientific reports
Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate a...

A Machine Learning Analysis of Big Metabolomics Data for Classifying Depression: Model Development and Validation.

Biological psychiatry
BACKGROUND: Many metabolomics studies of depression have been performed, but these have been limited by their scale. A comprehensive in silico analysis of global metabolite levels in large populations could provide robust insights into the pathologic...