Machine learning algorithm for early detection of end-stage renal disease.

ABSTRACT:

Background

End stage renal disease (ESRD) describes the most severe stage of chronic kidney disease (CKD), when patients need dialysis or renal transplant. There is often a delay in recognizing, diagnosing, and treating the various etiologies of CKD. The objective of the present study was to employ machine learning algorithms to develop a prediction model for progression to ESRD based on a large-scale multidimensional database.

Methods

This study analyzed 10,000,000 medical insurance claims from 550,000 patient records using a commercial health insurance database. Inclusion criteria were patients over the age of 18 diagnosed with CKD Stages 1-4. We compiled 240 predictor candidates, divided into six feature groups: demographics, chronic conditions, diagnosis and procedure fe

SUBMITTER: Segal Z 

PROVIDER: S-EPMC7693522 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature