Emergency Department Prediction of In-Hospital Mortality in Suspected Pulmonary Embolism: An Explainable Machine Learning Approach

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dc.contributor.authorFindik, Meliha
dc.contributor.authorAlatli, Tufan
dc.contributor.authorKocaoglu, Salih
dc.contributor.authorGelen, Yeltug Esra
dc.contributor.authorTas, Rahime Sema
dc.date.accessioned2026-04-25T14:20:28Z
dc.date.available2026-04-25T14:20:28Z
dc.date.issued2026
dc.departmentSinop Üniversitesi
dc.description.abstractBackground: Pulmonary embolism (PE) is a significant cause of cardiovascular mortality, and emergency department (ED) management requires early risk assessment to guide monitoring and disposition. Because key decisions are often needed while diagnostic evaluation is ongoing, the simplified Pulmonary Embolism Severity Index (sPESI) may provide limited discrimination for in-hospital outcomes. We evaluated whether explainable machine-learning (ML) models integrating routine ED variables with validated risk scores can predict in-hospital mortality in adults evaluated for suspected acute PE. Methods: A retrospective single-center cohort study was performed, including 220 consecutive adults evaluated for suspected acute PE in the ED between January 2021 and March 2025, comprising both PE-confirmed and PE-excluded cases. Predictors included demographics, vital signs, arterial blood gas indices, available imaging/echocardiographic findings, and Wells, Revised Geneva, and sPESI scores. Seven ML algorithms were trained and internally evaluated using the area under the receiver operating characteristic curve (AUC) and complementary metrics. Model interpretability was assessed using SHAP (SHAPley Additive exPlanations), and a sensitivity analysis was conducted in the PE-confirmed subgroup. Results: Tree-based ensemble models demonstrated higher discrimination for in-hospital all-cause mortality than simpler classifiers. SHAP analyses consistently highlighted sPESI, oxygenation/arterial blood gas indices, and malignancy as key contributors to mortality risk. Findings were similar in the PE-confirmed sensitivity analysis. Conclusions: Explainable ML models combining established risk scores with routinely collected ED variables may complement risk stratification along the suspected-PE pathway. External multicenter validation and prospective impact studies are warranted before clinical implementation.
dc.identifier.doi10.3390/jcm15041340
dc.identifier.issn2077-0383
dc.identifier.issue4
dc.identifier.orcid0000-0003-3757-2611
dc.identifier.orcid0000-0002-7570-4200
dc.identifier.pmid41753029
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/jcm15041340
dc.identifier.urihttps://hdl.handle.net/11486/8597
dc.identifier.volume15
dc.identifier.wosWOS:001700464000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofJournal of Clinical Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260420
dc.subjectpulmonary embolism
dc.subjectemergency department
dc.subjectmachine learning
dc.subjectmortality prediction
dc.subjectexplainable AI
dc.subjectSHAP
dc.subjectrisk stratification
dc.titleEmergency Department Prediction of In-Hospital Mortality in Suspected Pulmonary Embolism: An Explainable Machine Learning Approach
dc.typeArticle

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