Data were obtained from the Big Data Center at Taipei Veterans General Hospital (VGH). Using predictors such as estimated glomerular filtration rate, hemoglobin, urine protein-to-creatinine ratio, insulin use, β-blocker use, renin-angiotensin system inhibitor use, and hypertension, a machine learning-based predictive model was developed to generate end-stage renal disease risk labels for patients with chronic kidney disease among sepsis survivors [56]. This evidence concerns the gene INS and chronic kidney disease.