Clinical decision support system for detecting right ventricular dysfunction in acute pulmonary embolism: Explainable a new Binary Rule Search (BRS) ensembles and robustness evaluation

Background Right-ventricular dysfunction (RVD) in acute pulmonary embolism (PE) carries excess short-term mortality; fast, transparent risk stratification is needed. Objective To develop and evaluate an interpretable Binary Rule Search (BRS) framework for RVD detection on a fully binarized clinical dataset, emphasizing conservative robustness. Methods We analyzed a single-center cohort ( N = 363; development = 250, external validation = 113) recoded into 0/1 predictors. For subset sizes k = 1–5, a Rust BRS searched bit-mask rules maximizing Matthews correlation coefficient (MCC) on development data and was assessed over 500 stratified 250/113 hold-out repeats. We report MCC with bootstrap 95 % CIs plus sensitivity, specificity, precision, F1, and AUC-ROC. Robustness was summarized by the Stability-Bound Rule Score (SBRS). Results Performance increased with k; triplets offered the best parsimony–robustness balance, with modest conservative gains for quintets. The strongest quintet by point estimate was S{Age65_79; ThromMain; ThromBilat; DVTuni; Malig} (MCC = 0.326; 95 % CI 0.152–0.498). A balanced k = 4 rule, S{SexMale; ThromMain; Hypertens; HeartFail}, achieved Sensitivity 0.791, Specificity 0.557, F1 0.630, AUC-ROC 0.674 on external validation. Decision-tree checks yielded high lower-bound performance (e.g., L95 = 0.302 for S{ThromMain; DVTdist; COPD}), whereas LogNNet often matched mean MCC but showed consistently lower L95, indicating greater dispersion. PFI highlighted a thrombus-centric signal (ThromMain/ThromBilat/DVT) with meaningful secondary contributions from heart failure and hypertension. Conclusions Interpretable BRS ensembles deliver clinically acceptable, conservatively bounded performance using few binary predictors and transparent logic, supporting clinician-facing decision support in resource-constrained settings. We provide rule masks, uncertainty summaries, and an offline CDSS prototype; prospective multicenter validation is warranted. Copyright © 2025. Published by Elsevier B.V.