Development and Validation of a Machine Learning-Based Prediction Model for Detection of Biliary Atresia.

ABSTRACT:

Background and aims

Biliary atresia is a rare and devastating bile duct disease that occurs during the neonatal period. Timely identification and prompt surgical intervention is critical for improving the outcome. The aim of the study was to develop a new machine learning-based prediction model for the detection of biliary atresia.

Methods

Neonates aged <100 days with cholestasis at least once were retrospectively screened in 2 tertiary referral hospitals between 2015 and 2020. Simple demographic data, routine laboratory indices, and imaging findings of ultrasonography and hepatobiliary scintigraphy were used as features in the multivariate analysis. The extreme gradient boosting (XGBoost) framework was used to develop prediction models according to the diagnostic steps.

SUBMITTER: Choi HJ 

PROVIDER: S-EPMC11307559 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature