2026-10-19T00:27:14Zhttps://ebyu-rdlopen.rawdatalibrary.net/oai/request

oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/100772026-10-18T21:13:10Zopenairedrivercom_ebyu_10000col_ebyu_10001

Clinical decision support system for detecting right ventricular dysfunction in acute pulmonary embolism: Explainable a new Binary Rule Search (BRS) ensembles and robustness evaluationHuyut, Mehmet TahirVelichko, AndreiBelyaev, MaksimIzotov, YuriyTosun, MustafaSertoğullarından, BünyaminKaraoğlanoğlu, ŞebnemDemir, Abdussamed YasinKorzun, DmitryClinical decision supportInterpretable machine learningMatthews correlation coefficientPermutation feature importancePulmonary embolismRight ventricular dysfunctionRisk stratificationRule-based modelsAcute DiseaseAgedDecision Support Systems, ClinicalFemaleHumansMaleMiddle AgedROC CurveVentricular Dysfunction, RightArtificial intelligenceBiomedical signal processingDecision support systemsDecision treesLearning systemsPulmonary diseasesCorrelation coefficientMachine-learningMatthew correlation coefficientRight ventricular dysfunctionsaccuracyadultArticleBackground 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.Elsevier B.V.2026Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/10077https://doi.org/10.1016/j.artmed.2025.1033370933-3657enArtificial Intelligence in Medicineinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/10077