An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric...

Area of Science:

• Computational biology
• Machine learning in rare diseases
• Pediatric oncology

Background:

• Rare disease datasets, such as those for pediatric acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), present challenges for machine learning (ML) due to small sample sizes.
• Developing effective ML models for rare diseases requires specialized frameworks that can handle limited data.

Purpose of the Study:

• To create an interpretable ML framework for extracting actionable insights from small, tabular rare disease datasets.
• To apply this framework to predict treatment-related infections in pediatric AML and ALL.

Main Methods:

• The framework integrated optimized data imputation and sampling with supervised and unsupervised learning techniques.
• Literature-based discovery (LBD) using SemNet 2.0 was employed to identify additional predictive features.
• Interpretable ML models, including decision trees and regression models, were developed.

Main Results:

• An interpretable decision tree achieved ~79% accuracy in classifying infection risk (high/low) in pediatric ALL and AML.
• Regression models predicted the number of bacterial and viral infections with mean absolute errors of 2.26 and 1.29, respectively.
• Key predictive features included chemotherapy regimen, central nervous system involvement, leukemia type, and Down syndrome. LBD identified glucose, iron, and growth factors as additional predictors.

Conclusions:

• The developed ML framework provides state-of-the-art, interpretable predictions from rare disease datasets.
• This study establishes baseline ML model performance for predicting infections in pediatric AML and ALL.
• The framework demonstrates the potential for advancing ML applications in rare pediatric cancers.