AIMC Topic: Depressive Disorder

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Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis.

Human brain mapping
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
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
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
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
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
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
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...

Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error.

Systematic reviews
BACKGROUND: Here, we outline a method of applying existing machine learning (ML) approaches to aid citation screening in an on-going broad and shallow systematic review of preclinical animal studies. The aim is to achieve a high-performing algorithm ...

Ascertaining Depression Severity by Extracting Patient Health Questionnaire-9 (PHQ-9) Scores from Clinical Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The Patient Health Questionnaire-9 (PHQ-9) is a validated instrument for assessing depression severity. While some electronic health record (EHR) systems capture PHQ-9 scores in a structured format, unstructured clinical notes remain the only source ...