AIMC Topic: Depression
Clear Filters Showing 61 to 70 of 385 articles
Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.
Sensors (Basel, Switzerland)
Jan 12, 2025
Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its diverse and often overlapping clinical manifestations. In this context, electroencephalography (EEG) h...
Exploring the triglyceride-glucose index's role in depression and cognitive dysfunction: Evidence from NHANES with machine learning support.
Journal of affective disorders
Jan 11, 2025
BACKGROUND: Depression and cognitive impairments are prevalent among older adults, with evidence suggesting potential links to obesity and lipid metabolism disturbances. This study investigates the relationships between the triglyceride-glucose (TyG)...
Is Artificial Intelligence the Next Co-Pilot for Primary Care in Diagnosing and Recommending Treatments for Depression?
Medical sciences (Basel, Switzerland)
Jan 11, 2025
Depression poses significant challenges to global healthcare systems and impacts the quality of life of individuals and their family members. Recent advancements in artificial intelligence (AI) have had a transformative impact on the diagnosis and tr...
Application of machine learning in depression risk prediction for connective tissue diseases.
Scientific reports
Jan 11, 2025
This study retrospectively collected clinical data from 480 patients with connective tissue diseases (CTDs) at Nanjing First Hospital between August 2019 and December 2023 to develop and validate a multi-classification machine learning (ML) model for...
Schizophrenia more employable than depression? Language-based artificial intelligence model ratings for employability of psychiatric diagnoses and somatic and healthy controls.
PloS one
Jan 8, 2025
Artificial Intelligence (AI) assists recruiting and job searching. Such systems can be biased against certain characteristics. This results in potential misrepresentations and consequent inequalities related to people with mental health disorders. He...
Prediction of late-onset depression in the elderly Korean population using machine learning algorithms.
Scientific reports
Jan 7, 2025
Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims...
Machine learning algorithms to predict depression in older adults in China: a cross-sectional study.
Frontiers in public health
Jan 7, 2025
OBJECTIVE: The 2-fold objective of this research is to investigate machine learning's (ML) predictive value for the incidence of depression among China's older adult population and to determine the noteworthy aspects resulting in depression.
Beyond the hot flashes: how machine learning is uncovering the complexity of menopause-related depression.
CNS spectrums
Jan 6, 2025
BACKGROUND: The transition into menopause marks a significant stage in a woman's life, indicating the end of reproductive capability. This period, encompassing perimenopause and menopause, is characterized by declining levels of estrogen and progeste...
Utilising AI technique to identify depression risk among doctoral students.
Scientific reports
Dec 30, 2024
The phenomenon that the depression risk among doctoral students is higher than that of other groups should not be ignored. Despite this, studies specifically addressing depression risk in doctoral students are relatively scarce, and existing findings...
Estimating cardiovascular mortality in patients with hypertension using machine learning: The role of depression classification based on lifestyle and physical activity.
Journal of psychosomatic research
Dec 29, 2024
PURPOSE: This study aims to harness machine learning techniques, particularly the Random Survival Forest (RSF) model, to assess the impact of depression on cardiovascular disease (CVD) mortality among hypertensive patients. A key objective is to eluc...
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823