A clinical decision support system for predicting drug-related adverse effects in pediatric patients with congenital heart disease: Development and retrospective evaluation.

ObjectiveChildren 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.MethodsThis 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.ResultsThe 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.ConclusionsThis 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.