A Clinical Decision Support System for Predicting Drug-Related Adverse Effects in Pediatric Patients With Congenital Heart Disease: Development and Retrospective Evaluation Publisher Pubmed



Toni E ; Ayatollahi H ; Abbaszadeh R ; Fotuhi Siahpirani A
Source: Journal of International Medical Research Published:2026

Abstract

Objective: Children with congenital heart disease are highly vulnerable to drug-related adverse effects due to the use of complex polypharmacy. This study aimed to develop and retrospectively evaluate a hybrid clinical decision support system for predicting drug-related adverse effects in this population. Methods: This two-phase study combined machine-learning techniques and expert clinical rules. Phase 1 included a retrospective analysis of 4651 pediatric congenital heart disease reports from the Food and Drug Administration Adverse Event Reporting System to train and compare five machine-learning models. The best-performing model, Random Forest, was selected. In Phase 2, a hybrid clinical decision support system integrating the Random Forest model with an expert-validated rule-based engine was developed and retrospectively evaluated using 330 inpatient records of pediatric patients with congenital heart disease. Results: The Random Forest model achieved a mean area under the receiver operating characteristic curve of 0.902. During clinical validation, the hybrid clinical decision support system demonstrated a mean accuracy of 0.85 across 11 common drug–adverse effect pairs, outperforming standalone machine-learning– and rule-based approaches. This study demonstrated the feasibility and clinical fidelity of using a hybrid clinical decision support system for predicting drug-related adverse effects in pediatric patients with congenital heart disease, supporting safer and more personalized pharmacotherapy. Conclusions: This study demonstrated the feasibility and clinical fidelity of using a hybrid clinical decision support system for predicting drug-related adverse effects in pediatric patients with congenital heart disease, supporting safer and more personalized pharmacotherapy. © The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).