AIMC Topic: Disease Progression
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An Interpretable Population Graph Network to Identify Rapid Progression of Alzheimer's Disease Using UK Biobank.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
Alzheimer's disease (AD) manifests with varying progression rates across individuals, necessitating the understanding of their intricate patterns of cognition decline that could contribute to effective strategies for risk monitoring. In this study, w...
Towards Interpretable End-Stage Renal Disease (ESRD) Prediction: Utilizing Administrative Claims Data with Explainable AI Techniques.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
This study explores the potential of utilizing administrative claims data, combined with advanced machine learning and deep learning techniques, to predict the progression of Chronic Kidney Disease (CKD) to End-Stage Renal Disease (ESRD). We analyze ...
[Application of artificial intelligence in glaucoma. Part 2. Neural networks and machine learning in the monitoring and treatment of glaucoma].
Vestnik oftalmologii
Jan 1, 2024
The second part of the literature review on the application of artificial intelligence (AI) methods for screening, diagnosing, monitoring, and treating glaucoma provides information on how AI methods enhance the effectiveness of glaucoma monitoring a...
GAN Learning Methods for Bulk RNA-Seq Data and Their Interpretive Application in the Context of Disease Progression.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
A generative adversarial network (GAN) is a generative model that consists of two adversarial networks, a discriminator and a generator, usually in the form of neural networks. One of the useful things about applying GANs is that they can synthesize ...
Updated Models of Alzheimer's Disease with Deep Neural Networks.
Journal of Alzheimer's disease : JAD
Jan 1, 2024
BACKGROUND: In recent years, researchers have focused on developing precise models for the progression of Alzheimer's disease (AD) using deep neural networks. Forecasting the progression of AD through the analysis of time series data represents a pro...
Prognostic Power? Do the Plasma Biomarkers, Neurofilament Light and Phospho-Tau 181, Improve Prediction of Progression to Alzheimer's Disease Using a Machine Learning Approach in the ADNI Cohort?
Journal of Alzheimer's disease : JAD
Jan 1, 2024
With the advent of therapeutics with potential to slow Alzheimer's disease progression the necessity of understanding the diagnostic value of plasma biomarkers is critical, not only for understanding the etiology and progression of Alzheimer's diseas...
Simplifying Alzheimer's Disease Monitoring: A Novel Machine-Learning Approach to Estimate the Clinical Dementia Rating Sum of Box Changes Using the Mini-Mental State Examination and Functional Activities Questionnaire.
Journal of Alzheimer's disease : JAD
Jan 1, 2024
BACKGROUND: Primary outcome measure in the clinical trials of disease modifying therapy (DMT) drugs for Alzheimer's disease (AD) has often been evaluated by Clinical Dementia Rating sum of boxes (CDRSB). However, CDR testing requires specialized trai...
Artificial Intelligence in The Management of Neurodegenerative Disorders.
CNS & neurological disorders drug targets
Jan 1, 2024
Neurodegenerative disorders are characterized by a gradual but irreversible loss of neurological function. The ability to detect and treat these conditions successfully is crucial for ensuring the best possible quality of life for people who suffer f...
Retraction to: “Performance Analysis of Alexnet for Classification of Knee Osteoarthritis.
Current medical imaging
Jan 1, 2024
UNLABELLED: It has come to the publisher’s attention that the article is a duplication of a published paper in another journal, NeuroQuantology, available at the following link: https://neuroquantology.com/media/article_pdfs/1686-1692.pdf This raises...
An online framework for survival analysis: reframing Cox proportional hazards model for large data sets and neural networks.
Biostatistics (Oxford, England)
Dec 15, 2023
In many biomedical applications, outcome is measured as a "time-to-event" (e.g., disease progression or death). To assess the connection between features of a patient and this outcome, it is common to assume a proportional hazards model and fit a pro...
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