AIMC Topic: Disease Progression
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Long-Term Efficacy of an AI-Based Health Coaching Mobile App in Slowing the Progression of Nondialysis-Dependent Chronic Kidney Disease: Retrospective Cohort Study.
Journal of medical Internet research
Nov 25, 2024
BACKGROUND: Chronic kidney disease (CKD) is a significant public health concern. Therefore, practical strategies for slowing CKD progression and improving patient outcomes are imperative. There is limited evidence to substantiate the efficacy of mobi...
Establishing a machine learning dementia progression prediction model with multiple integrated data.
BMC medical research methodology
Nov 22, 2024
OBJECTIVE: Dementia is a significant medical and social issue in most developed countries. Practical tools for predicting the progression of degenerative dementia are highly valuable. Machine learning (ML) methods facilitate the construction of effec...
Detection of glaucoma progression on longitudinal series of en-face macular optical coherence tomography angiography images with a deep learning model.
The British journal of ophthalmology
Nov 22, 2024
BACKGROUND/AIMS: To design a deep learning (DL) model for the detection of glaucoma progression with a longitudinal series of macular optical coherence tomography angiography (OCTA) images.
Deep Learning
ROC Curve
Follow-Up Studies
Tomography, Optical Coherence
Male
Middle Aged
Disease Progression
Fluorescein Angiography
Macula Lutea
Retinal Vessels
Glaucoma, Open-Angle
Optic Disk
Retinal Ganglion Cells
Visual Fields
Visual Field Tests
Retrospective Studies
Humans
Female
Aged
Intraocular Pressure
Quantification of coronary artery calcification in systemic sclerosis using visual ordinal and deep learning scoring: Association with systemic sclerosis clinical features.
Seminars in arthritis and rheumatism
Nov 20, 2024
OBJECTIVE: To investigate the association between systemic sclerosis (SSc) clinical features and the extent and progression of coronary artery calcifications.
A Transcriptomics-Based Machine Learning Model Discriminating Mild Cognitive Impairment and the Prediction of Conversion to Alzheimer's Disease.
Cells
Nov 19, 2024
The clinical spectrum of Alzheimer's disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or severe AD. Although a few disease-modifying treatments, such as lecanemab and donanemab, have been develo...
DAPNet: multi-view graph contrastive network incorporating disease clinical and molecular associations for disease progression prediction.
BMC medical informatics and decision making
Nov 19, 2024
BACKGROUND: Timely and accurate prediction of disease progress is crucial for facilitating early intervention and treatment for various chronic diseases. However, due to the complicated and longitudinal nature of disease progression, the capacity and...
Artificial intelligence in assessing progression of age-related macular degeneration.
Eye (London, England)
Nov 18, 2024
The human population is steadily growing with increased life expectancy, impacting the prevalence of age-dependent diseases, including age-related macular degeneration (AMD). Health care systems are confronted with an increasing burden with rising pa...
Predicting Asthma Exacerbations Using Machine Learning Models.
Advances in therapy
Nov 18, 2024
INTRODUCTION: Although clinical, functional, and biomarker data predict asthma exacerbations, newer approaches providing high accuracy of prognosis are needed for real-world decision-making in asthma. Machine learning (ML) leverages mathematical and ...
Machine-learning methodologies to predict disease progression in chronic hepatitis B in Africa.
Hepatology communications
Nov 15, 2024
BACKGROUND: Little is known about the determinants of disease progression among African patients with chronic HBV infection.
A machine learning model for predicting worsening renal function using one-year time series data in patients with type 2 diabetes.
Journal of diabetes investigation
Nov 13, 2024
BACKGROUND AND AIMS: To prevent end-stage renal disease caused by diabetic kidney disease, we created a predictive model for high-risk patients using machine learning.
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