Development of Error Passing Network for Optimizing the Prediction of VO$_2$ peak in Childhood Acute Leukemia Survivors

Nicolas Raymond, Hakima Laribi, Maxime Caru, Mehdi Mitiche, Valerie Marcil, Maja Krajinovic, Daniel Curnier, Daniel Sinnett, Martin Vallières

View paper (PDF)

Abstract: Approximately two-thirds of survivors of childhood acute lymphoblastic leukemia (ALL) cancer develop late adverse effects post-treatment. Prior studies explored prediction models for personalized follow-up, but none integrated the usage of neural networks to date. In this work, we propose the Error Passing Network (EPN), a graph-based method that leverages relationships between samples to propagate residuals and adjust predictions of any machine learning model. We tested our approach to estimate patients' VO$_2$ peak, a reliable indicator of their cardiac health. We used the EPN in conjunction with several baseline models and observed up to $12.16$% improvement in the mean average percentage error compared to the last established equation predicting VO$_2$ peak in childhood ALL survivors. Along with this performance improvement, our final model is more efficient considering that it relies only on clinical variables that can be self-reported by patients, therefore removing the previous need of executing a resource-consuming physical test.