Gamma-glutamyl transpeptidase has different efficacy on biliary atresia diagnosis in different hospital patient groups: an application of machine learning approach.

Objectives: Gamma-glutamyl transpeptidase (GGT) is the most widely used biomarker in the early diagnosis of biliary atresia (BA), but its diagnostic efficacy is questionable in different sub-populations. We aim to identify subgroups that are defined by specific variables with cut-offs and can significantly affect the diagnostic efficacy of GGT for detecting BA.

Methods: Clinical data from 1273 infants with neonatal obstructive jaundice (NOJ) diagnosed between January 2012 and December 2017 at the Children's Hospital of Fudan University were enrolled, reviewed, and analyzed.

Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction.

Background: Missing data are common in statistical analyses, and imputation methods based on random forests (RF) are becoming popular for handling missing data especially in biomedical research. Unlike standard imputation approaches, RF-based imputation methods do not assume normality or require specification of parametric models. However, it is still inconclusive how they perform for non-normally distributed data or when there are non-linear relationships or interactions.