Automated sex and age partitioning for the estimation of reference intervals using a regression tree model

Abstract

Reference intervals (RI) play a decisive role in the interpretation of medical laboratory results. An important step in the determination of RI is age- and sex specific partitioning, which is usually based on an empirical approach by graphical representation. In this study, we evaluate an automated machine learning approach.

Keywords

• reference intervals
• direct methods
• machine learning
• age and sex partitioning
• regression tree model