Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients.

ABSTRACT: Starting renal replacement therapy (RRT) for patients with chronic kidney disease (CKD) at an optimal time, either with hemodialysis or kidney transplantation, is crucial for patient's well-being and for successful management of the condition. In this paper, we explore the possibilities of creating forecasting models to predict the onset of RRT 3, 6, and 12 months from the time of the patient's first diagnosis with CKD, using only the comorbidities data from National Health Insurance from Taiwan. The goal of this study was to see whether a limited amount of data (including comorbidities but not considering laboratory values which are expensive to obtain in low- and medium-income countries) can provide a good basis for such predictive models. On the other hand, in developed countries, such

SUBMITTER: Dovgan E 

PROVIDER: S-EPMC7274378 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature