An early prediction model for canine chronic kidney disease based on routine clinical laboratory tests.

The aim of this study was to derive a model to predict the risk of dogs developing chronic kidney disease (CKD) using data from electronic health records (EHR) collected during routine veterinary practice. Data from 57,402 dogs were included in the study. Two thirds of the EHRs were used to build the model, which included feature selection and identification of the optimal neural network type and architecture.

Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning.

Background: Advanced machine learning methods combined with large sets of health screening data provide opportunities for diagnostic value in human and veterinary medicine.

Hypothesis/objectives: To derive a model to predict the risk of cats developing chronic kidney disease (CKD) using data from electronic health records (EHRs) collected during routine veterinary practice.

Animals: A total of 106 251 cats that attended Banfield Pet Hospitals between January 1, 1995, and December 31, 2017.