AIMC Topic: Depressive Disorder
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Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review.
Journal of affective disorders
Jun 16, 2024
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
Feb 24, 2024
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
Dec 22, 2023
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...
Deconstructing depression by machine learning: the POKAL-PSY study.
European archives of psychiatry and clinical neuroscience
Dec 13, 2023
Unipolar depression is a prevalent and disabling condition, often left untreated. In the outpatient setting, general practitioners fail to recognize depression in about 50% of cases mainly due to somatic comorbidities. Given the significant economic,...
Predicting Outcomes of Antidepressant Treatment in Community Practice Settings.
Psychiatric services (Washington, D.C.)
Dec 5, 2023
OBJECTIVE: The authors examined whether machine-learning models could be used to analyze data from electronic health records (EHRs) to predict patients' responses to antidepressant medications.
Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning.
Sensors (Basel, Switzerland)
Oct 23, 2023
Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and sub...
Evaluation of Effect of Curcumin on Psychological State of Patients with Pulmonary Hypertension by Magnetic Resonance Image under Deep Learning.
Contrast media & molecular imaging
Jul 26, 2021
This research aimed to evaluate the right ventricular segmentation ability of magnetic resonance imaging (MRI) images based on deep learning and evaluate the influence of curcumin (Cur) on the psychological state of patients with pulmonary hypertensi...
Convolutional Neural Network-Based Deep Learning Model for Predicting Differential Suicidality in Depressive Patients Using Brain Generalized q-Sampling Imaging.
The Journal of clinical psychiatry
Feb 23, 2021
OBJECTIVE: Suicide is a priority health problem. Suicide assessment depends on imperfect clinician assessment with minimal ability to predict the risk of suicide. Machine learning/deep learning provides an opportunity to detect an individual at risk ...
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 ...
Precision psychiatry in clinical practice.
International journal of psychiatry in clinical practice
Aug 27, 2020
The treatment of depression represents a major challenge for healthcare systems and choosing among the many available drugs without objective guidance criteria is an error-prone process. Recently, pharmacogenetic biomarkers entered in prescribing gui...
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