AIMC Topic: Diabetic Nephropathies
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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.
Unveiling the utility of artificial intelligence for prediction, diagnosis, and progression of diabetic kidney disease: an evidence-based systematic review and meta-analysis.
Current medical research and opinion
Nov 13, 2024
OBJECTIVE: The purpose of this study was to conduct a systematic investigation of the potential of artificial intelligence (AI) models in the prediction, detection of diagnostic biomarkers, and progression of diabetic kidney disease (DKD). In additio...
Development and external validation of a machine learning model to predict diabetic nephropathy in T1DM patients in the real-world.
Acta diabetologica
Nov 11, 2024
AIMS: Studies on machine learning (ML) for the prediction of diabetic nephropathy (DN) in type 1 diabetes mellitus (T1DM) patients are rare. This study focused on the development and external validation of an explainable ML model to predict the risk ...
Development of a machine learning model for precision prognosis of rapid kidney function decline in people with diabetes and chronic kidney disease.
Diabetes research and clinical practice
Oct 19, 2024
AIMS: To develop a machine learning model for predicting rapid kidney function decline in people with type 2 diabetes (T2D) and chronic kidney disease (CKD) and to pinpoint key modifiable risk factors for targeted interventions.
Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal Annotations.
Journal of the American Society of Nephrology : JASN
Oct 9, 2024
BACKGROUND: Deep learning has great potential in digital kidney pathology. However, its effectiveness depends heavily on the availability of extensively labeled datasets, which are often limited because of the specialized knowledge and time required ...
Integrated approach of machine learning, Mendelian randomization and experimental validation for biomarker discovery in diabetic nephropathy.
Diabetes, obesity & metabolism
Oct 6, 2024
AIM: To identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by integrating machine learning, Mendelian randomization (MR) and experimental validation.
Identification of common biomarkers in diabetic kidney disease and cognitive dysfunction using machine learning algorithms.
Scientific reports
Sep 27, 2024
Cognitive dysfunction caused by diabetes has become a serious global medical issue. Diabetic kidney disease (DKD) exacerbates cognitive dysfunction in patients, although the precise mechanism behind this remains unclear. Here, we conducted an investi...
Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning (Diabetes Metab J 2024;48:771-9).
Diabetes & metabolism journal
Sep 1, 2024
Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning (Diabetes Metab J 2024;48:771-9).
Diabetes & metabolism journal
Sep 1, 2024
Identification of key immune-related genes and potential therapeutic drugs in diabetic nephropathy based on machine learning algorithms.
BMC medical genomics
Aug 26, 2024
BACKGROUND: Diabetic nephropathy (DN) is a major contributor to chronic kidney disease. This study aims to identify immune biomarkers and potential therapeutic drugs in DN.
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