AIMC Topic: Depression

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[Prediction of depression symptoms in seniors and analysis of influencing factors based on explainable machine learning].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi
This study aims to construct a machine learning model to predict depression symptoms in the elderly and analyze the key influencing factors of depression in the elderly using the shapley additive interpretation (SHAP) method. Based on entries from ...

Identification of Depression Subtypes in Parkinson's Disease Patients via Structural MRI Whole-Brain Radiomics: An Unsupervised Machine Learning Study.

CNS neuroscience & therapeutics
OBJECTIVE: Current clinical evaluation may tend to lack precision in detecting depression in Parkinson's disease (DPD). Radiomics features have gradually shown potential as auxiliary diagnostic tools in identifying and distinguishing different subtyp...

A psychologically interpretable artificial intelligence framework for the screening of loneliness, depression, and anxiety.

Applied psychology. Health and well-being
Negative emotions such as loneliness, depression, and anxiety (LDA) are prevalent and pose significant challenges to emotional well-being. Traditional methods of assessing LDA, reliant on questionnaires, often face limitations because of participants...

Acoustic-based machine learning approaches for depression detection in Chinese university students.

Frontiers in public health
BACKGROUND: Depression is major global public health problems among university students. Currently, the evaluation and monitoring of depression predominantly depend on subjective and self-reported methods. There is an urgent necessity to develop obje...

Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study.

PloS one
CONTEXT AND BACKGROUND: Depression has affected millions of people worldwide and has become one of the most common mental disorders. Early mental disorder detection can reduce costs for public health agencies and prevent other major comorbidities. Ad...

Predicting depression severity using machine learning models: Insights from mitochondrial peptides and clinical factors.

PloS one
Depression presents a significant challenge to global mental health, often intertwined with factors including oxidative stress. Although the precise relationship with mitochondrial pathways remains elusive, recent advances in machine learning present...

Analysis of In-Home Movement Patterns for Depression Assessment in Older Adults - A Feasibility Study.

Studies in health technology and informatics
Depression significantly impacts the wellbeing of older Australians, posing considerable challenges to their overall quality of life. This study aimed to detect in-home movement patterns of participants that could be indicative of depressive states. ...

Machine learning for detection of heterogeneous effects of Medicaid coverage on depression.

American journal of epidemiology
In 2008, Oregon expanded its Medicaid program using a lottery, creating a rare opportunity to study the effects of Medicaid coverage using a randomized controlled design (Oregon Health Insurance Experiment). Analysis showed that Medicaid coverage low...

A Sentiment Pre-trained Text-Guided Multimodal Cross-Attention Transformer for Improved Depression Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Depression is a widespread mental health issue requiring efficient automated detection methods. Traditional single-modality approaches are less effective due to the disorder's complexity, leading to a focus on multimodal analysis. Recent advancements...

TAU-DI Net: A Multi-Scale Convolutional Network Combining Prob-Sparse Attention for EEG-based Depression Identification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
EEG-based detection of major depression disorder (MDD) plays a pivotal role in the subsequent treatment and recovery. With the rapid development of deep learning, CNN, LSTM, and attention-based models have been used for auxiliary diagnosis of MDD fro...