AIMC Topic: Depressive Disorder
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Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM).
European psychiatry : the journal of the Association of European Psychiatrists
Nov 1, 2016
BACKGROUND: Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depress...
Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.
PloS one
Feb 5, 2016
BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took acco...
Reflections of Low-Income, Second-Generation Latinas About Experiences in Depression Therapy.
Qualitative health research
Jan 29, 2016
Depression is higher among second-generation Latinas compared with immigrants, but mental health treatment is stigmatized. Therefore, second-generation Latinas were interviewed after completing an eight-session depression treatment program to gain in...
Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction.
International journal of geriatric psychiatry
Feb 17, 2015
OBJECTIVE: Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate ...
From the -Factor to Cognitive Content: Detection and Discrimination of Psychopathologies Based on Explainable Artificial Intelligence.
Depression and anxiety
Jan 1, 2025
Differentiating psychopathologies is challenging due to shared underlying mechanisms, such as the -factor. Nevertheless, recent methodological advances suggest that distinct linguistic markers can help detect and differentiate these conditions. This...
Exploring Self-Supervised Models for Depressive Disorder Detection: A Study on Speech Corpora.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Automatic detection of depressive disorder from speech signals can help improve medical diagnosis reliability. However, a significant challenge in this field is that most of the available depression datasets are relatively small, which limits the eff...
[Identifying Depressive Disorder With Sleep Electroencephalogram Data: A Study Based on Deep Learning].
Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
Mar 1, 2023
OBJECTIVE: To explore the effectiveness of using deep learning network combined Vision Transformer (ViT) and Transformer to identify patients with depressive disorder on the basis of their sleep electroencephalogram (EEG) signals.
Discriminating Heterogeneous Trajectories of Resilience and Depression After Major Life Stressors Using Polygenic Scores.
JAMA psychiatry
Jul 1, 2021
IMPORTANCE: Major life stressors, such as loss and trauma, increase the risk of depression. It is known that individuals show heterogeneous trajectories of depressive symptoms following major life stressors, including chronic depression, recovery, an...
Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression.
JAMA psychiatry
Feb 1, 2021
IMPORTANCE: Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to yo...
Depression screening using mobile phone usage metadata: a machine learning approach.
Journal of the American Medical Informatics Association : JAMIA
Apr 1, 2020
OBJECTIVE: Depression is currently the second most significant contributor to non-fatal disease burdens globally. While it is treatable, depression remains undiagnosed in many cases. As mobile phones have now become an integral part of daily life, th...
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