Artificial intelligence in medicine · 2025
Clinical decision support system for detecting right ventricular dysfunction in acute pulmonary embolism: Explainable a new Binary Rule Search (BRS) ensembles and robustness evaluation.
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Huyut Mehmet Tahir, Velichko Andrei, Belyaev Maksim, Izotov Yuriy, Tosun Mustafa, Sertoğullarından Bünyamin, Karaoğlanoğlu Şebnem, Demir Abdussamed Yasin, Korzun Dmitry
Аннотация
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.
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.