Dharma: A novel, clinically grounded machine learning framework for pediatric appendicitis-Diagnosis, severity assessment and evidence-based clinical decision support.

ABSTRACT: Acute appendicitis is a common but diagnostically challenging surgical emergency in children. Existing linear scoring systems lack sufficient accuracy for standalone use, while advanced imaging is constrained by risks of sedation, contrast, and radiation. Furthermore, no available tools provide prognostic guidance. We introduce Dharma, a machine learning framework consisting of a clinically grounded imputer and two random forest classifiers for diagnosis and severity assessment. Designed for real-world bedside use, Dharma is open-sourced and accessible through a web application. Dharma achieved excellent diagnostic performance, with an AUC-ROC of 0.98 [0.97-0.99] and accuracy of 93% [91-95]. For prognostic classification, it identified complicated appendicitis with high sensitivity (96% [9

SUBMITTER: Thapa Kshetri A 

PROVIDER: S-EPMC12822984 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature