Chronic Kidney Disease
Chronic kidney disease (CKD) research focuses on improving early detection and prediction of disease progression, particularly to end-stage renal disease (ESRD), to enable timely interventions and reduce healthcare disparities. Current research heavily utilizes machine learning, employing various algorithms like Random Forest, XGBoost, LSTM networks, and even transformer models, often incorporating explainable AI techniques to enhance model interpretability and reduce bias in predictions. These advancements aim to improve the accuracy and clinical utility of predictive models, ultimately leading to better patient outcomes and more efficient resource allocation within healthcare systems.
Papers
Predicting Long-term Renal Impairment in Post-COVID-19 Patients with Machine Learning Algorithms
Maitham G. Yousif, Hector J. Castro, John Martin, Hayder A. Albaqer, Fadhil G. Al-Amran, Habeeb W. Shubber, Salman Rawaf
Supervised Learning Models for Early Detection of Albuminuria Risk in Type-2 Diabetes Mellitus Patients
Arief Purnama Muharram, Dicky Levenus Tahapary, Yeni Dwi Lestari, Randy Sarayar, Valerie Josephine Dirjayanto