Developing an explainable machine learning model to predict false-negative citrin deficiency cases in newborn screening.

ABSTRACT:

Background

Neonatal Intrahepatic Cholestasis caused by Citrin Deficiency (NICCD) is an autosomal recessive disorder affecting the urea cycle and energy metabolism. Newborn screening (NBS) usually relies on elevated citrulline, but some patients have normal citrulline, resulting in false negatives and delayed diagnosis. This study develops an explainable machine learning (ML) model to predict false-negative NICCD cases during NBS.

Methods

Data from 53 false-negative NICCD patients and 212 controls, collected retrospectively between 2011 and 2024, were analyzed. The dataset was split into a training set (70%) and a test set (30%). External validation involved 48 participants from distinct time periods. Key predictors were identified using variable importance in projection (VIP

SUBMITTER: Wang P 

PROVIDER: S-EPMC12505637 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature