Machine learning-based prediction of health outcomes in pediatric organ transplantation recipients

Objectives: Prediction of post-transplant health outcomes and identification of key factors remain important issues for pediatric transplant teams and researchers. Outcomes research has generally relied on general linear modeling or similar techniques offering limited predictive validity. Thus far, data-driven modeling and machine learning (ML) approaches have had limited application and success in pediatric transplant outcomes research. The purpose of the current study was to examine ML models predicting post-transplant hospitalization in a sample of pediatric kidney, liver, and heart transplant recipients from a large solid organ transplant program. Materials and Methods: Various logistic regression, naive Bayes, support vector machine, and deep learning (DL) methods were used to predict 1-, 3-, and 5-year post-transplant hospitalization using patient and administrative data from a large pediatric organ transplant center. Results: DL models did not outperform traditional ML models across organ types and prediction windows with area under the receiver operating characteristic curve values ranging from 0.50 to 0.593. Shapley additive explanations (SHAP) were used to increase the interpretability of DL model results. Various medical, patient, and social variables were identified as salient predictors across organ types. Discussion: Results showed that deep learning models did not yield superior performance in comparison to models using traditional machine learning methods. However, the potential utility of deep learning modeling for health outcome prediction with pediatric patients in the presence of large number of samples warrants further examination. Conclusion: Results point to DL models as potentially useful tools in decision-support systems assisting physicians and transplant teams in identifying patients at a greater risk for poor post-transplant outcomes.