Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease.
Yutong ZouLijun ZhaoJunlin ZhangYiting WangYucheng WuHonghong RenTingli WangRui ZhangJiali WangYuancheng ZhaoChunmei QinHuan XuLin LiZhonglin ChaiMark E CooperNanwei TongFang LiuPublished in: Renal failure (2022)
In contrast with the treatment of participants with early-phase T2DM with or without mild kidney damage, major emphasis should be placed on indicators of kidney function, nutrition, anemia, and proteinuria for participants with T2DM and advanced DKD to delay ESRD, rather than age, sex, and control of hypertension and glycemia.
Keyphrases
- end stage renal disease
- chronic kidney disease
- machine learning
- peritoneal dialysis
- blood pressure
- type diabetes
- artificial intelligence
- magnetic resonance
- deep learning
- physical activity
- oxidative stress
- big data
- glycemic control
- metabolic syndrome
- magnetic resonance imaging
- adipose tissue
- wound healing
- combination therapy
- iron deficiency