AIMC Topic: Depressive Disorder
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Detection of Depression and Scaling of Severity Using Six Channel EEG Data.
Journal of medical systems
May 21, 2020
Depression is a psychiatric problem which affects the growth of a person, like how a person thinks, feels and behaves. The major reason behind wrong diagnosis of depression is absence of any laboratory test for detection as well as severity scaling o...
Fitting prediction rule ensembles to psychological research data: An introduction and tutorial.
Psychological methods
Feb 10, 2020
Prediction rule ensembles (PREs) are a relatively new statistical learning method, which aim to strike a balance between predictive performance and interpretability. Starting from a decision tree ensemble, like a boosted tree ensemble or a random for...
Developing algorithms to predict adult onset internalizing disorders: An ensemble learning approach.
Journal of psychiatric research
Dec 6, 2019
A growing literature is utilizing machine learning methods to develop psychopathology risk algorithms that can be used to inform preventive intervention. However, efforts to develop algorithms for internalizing disorder onset have been limited. The g...
Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis.
Human brain mapping
Oct 1, 2019
Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity ...
A deep learning framework for automatic diagnosis of unipolar depression.
International journal of medical informatics
Sep 27, 2019
BACKGROUND AND PURPOSE: In recent years, the development of machine learning (ML) frameworks for automatic diagnosis of unipolar depression has escalated to a next level of deep learning frameworks. However, this idea needs further validation. Theref...
Individualized prediction of depressive disorder in the elderly: A multitask deep learning approach.
International journal of medical informatics
Sep 23, 2019
INTRODUCTION: Depressive disorder is one of the major public health problems among the elderly. An effective depression risk prediction model can provide insights on the disease progression and potentially inform timely targeted interventions. Theref...
The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis.
International journal of environmental research and public health
Jun 18, 2019
Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no systematic review or bibliometric analysis in the medical literature about the applications of AI in depre...
See your mental state from your walk: Recognizing anxiety and depression through Kinect-recorded gait data.
PloS one
May 22, 2019
As the challenge of mental health problems such as anxiety and depression increasing today, more convenient, objective, real-time assessing techniques of mental state are in need. The Microsoft Kinect camera is a possible option for contactlessly cap...
Detecting depression using a framework combining deep multimodal neural networks with a purpose-built automated evaluation.
Psychological assessment
May 2, 2019
Machine learning (ML) has been introduced into the medical field as a means to provide diagnostic tools capable of enhancing accuracy and precision while minimizing laborious tasks that require human intervention. There is mounting evidence that the ...
Artificial intelligence based discovery of the association between depression and chronic fatigue syndrome.
Journal of affective disorders
Mar 8, 2019
BACKGROUND: Both of the modern medicine and the traditional Chinese medicine classify depressive disorder (DD) and chronic fatigue syndrome (CFS) to one type of disease. Unveiling the association between depressive and the fatigue diseases provides a...
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