Learning Medical Risk Scores for Pediatric Appendicitis

Abstract

Appendicitis is a common childhood disease, the management of which still lacks consolidated international criteria. In clinical practice, heuristic scoring systems are often used to assess the urgency of patients with suspected appendicitis. Previous work on machine learning for appendicitis has focused on conventional classification models, such as logistic regression and tree-based ensembles. In this study, we investigate the use of risk supersparse linear integer models (risk SLIM) for learning data-driven risk scores to predict the diagnosis, management, and complications in pediatric patients with suspected appendicitis on a dataset consisting of 430 children from a tertiary care hospital. We demonstrate the efficacy of our approach and compare the performance of learnt risk scores to previous analyses with random forests. Risk SLIM is able to detect medically meaningful features and outperforms the traditional appendicitis scores, while at the same time is better suited for the clinical setting than tree-based ensembles.