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A prediction model for genetic cholestatic disease in infancy using the machine learning approach
Chi-San Tai1, Sung-Chu Ko2, Chien-Chang Lee2,3
1Department of Pediatrics, National Taiwan University Children's Hospital, Taipei, Taiwan.
Insights
Machine learning models can predict genetic causes of infant cholestasis. The Jaundice Diagnosis Easy for Baby (JADE-B) tool uses 20 clinical parameters to identify infants needing genetic testing, optimizing resource use.
Area of Science:
- Pediatric Hepatology
- Medical Informatics
- Genetics
Background:
- Infant cholestasis diagnosis is challenging, particularly for genetic causes.
- Timely identification of genetic etiologies is crucial for effective pediatric liver disease management.
Purpose of the Study:
- To develop machine learning (ML) models, named Jaundice Diagnosis Easy for Baby (JADE-B), for predicting genetic causes of cholestasis in infants.
- To identify infants at higher risk for genetic disorders, enabling targeted diagnostic approaches.
Main Methods:
- Utilized a tertiary medical center's database (2006-2018) with 1845 infants diagnosed with cholestatic liver disease.
- Employed 47 clinical and laboratory parameters to train four ML classifiers: Logistic Regression, XGBoost, LightGBM, and Random Forests.
- Developed a refined JADE-B model using 20 accessible parameters for early prediction.
Main Results:
- Included 1008 infants under 1 year with cholestatic liver disease in the analysis.
- Achieved high performance with models yielding an area under the receiver operating characteristic curve ranging from 0.836 to 0.884.
- The final JADE-B model effectively predicts genetic disorders using 20 parameters within the first month of disease onset.
Conclusions:
- The JADE-B ML model aids in prioritizing infants for genetic testing and specialist referrals.
- Optimizes the allocation of genetic diagnostic resources for pediatric cholestasis.
- Facilitates earlier and more accurate identification of genetic liver diseases in infants.
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.
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