A prediction model for genetic cholestatic disease in infancy using the machine learning approach
Issued Date
2025-01-01
Resource Type
ISSN
02772116
eISSN
15364801
Scopus ID
2-s2.0-105012140420
Journal Title
Journal of Pediatric Gastroenterology and Nutrition
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Pediatric Gastroenterology and Nutrition (2025)
Suggested Citation
Tai C.S., Ko S.C., Lee C.C., Yang H.R., Lin C.R., Choe B.H., Treepongkaruna S., Chongsrisawat V., Wu C.C., Chen H.L. A prediction model for genetic cholestatic disease in infancy using the machine learning approach. Journal of Pediatric Gastroenterology and Nutrition (2025). doi:10.1002/jpn3.70166 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/111558
Title
A prediction model for genetic cholestatic disease in infancy using the machine learning approach
Corresponding Author(s)
Other Contributor(s)
Abstract
Objectives: Cholestasis in infancy poses a complex clinical conundrum for pediatric hepatologists, warranting timely diagnosis, especially for genetic diseases. This study aims to create machine learning (ML)-based prediction models, referred to as Jaundice Diagnosis Easy for Baby (JADE-B), to identify the subjects prone to genetic causes of cholestasis. Methods: We retrieved patient data from the Integrated Medical Database at a university-affiliated tertiary medical center from 2006 to 2018. Patients with cholestatic disease were identified using liver-disease-specific International Classification of Diseases codes. A total of 47 clinical and laboratory parameters were used for ML for predicting a positive genetic disease, defined by a disease-specific genetic diagnosis matched with phenotype. Four distinct classifiers: Logistic regression, XGBoost (XGB), LightGBM (LGBM), and Random Forests were utilized to build the models. Results: From a patient pool of 1845, 1008 infants below 1 year of age diagnosed with cholestatic liver disease were included in the analysis. A comprehensive set of 47 pertinent clinical and laboratory features was incorporated for training the ML models. We built five sets of models (Model 1-5), yielding an area under the receiver operating characteristic curve of 0.869, 0.884, 0.855, 0.852, and 0.836, respectively. A JADE-B model was built using 20 simple and widely accessible clinical parameters at disease onset, up to 1 month, to predict patients with genetic disorders. Conclusions: The machine learning model prioritizes cholestatic infants for the allocation of genetic diagnostic tools and patient referrals, as well as optimizes the utilization of genetic diagnostic resources.