Göğüs Ağrılı Hastaya Yaklaşım – Kardiyak Aciller Tufan ALATLI Göğüs Ağrılı Hastaya Yaklaşım Pulmoner AcillerSalih KOCAOĞLU Arrest Hastaya YaklaşımMuhammet ÇAKAS Senkop-PresenkopMeliha FINDIKÖzge KAMA BAŞCI Bradikardi TaşikardiRahime Sema TAŞ Nefes DarlığıKübra Nur GÜNEYSelman GÜMÜŞ Karın AğrısıFerhat ÇAY Bulantı-KusmaYağmur Ecem NURAYDIN Hematemez, Melena, HematokezyaAhmet Buğra ÖNLER HemoptiziHilal PINAR AteşHayrullah YURDAKUL Baş AğrısıSemih TAŞKINLAR Bilinç Bozukluğuna YaklaşımYeltuğ Esra GELEN Baş Dönmesine Acilde Yaklaşımİlker ÇERMİKLİ Hemiparezi, Hemipleji, ParaplejiFeyza Nur USLURahime Sema TAŞ Anemiye YaklaşımYaşar ŞİŞMAN Hipertansif Hastaya YaklaşımHarun GÜNEŞTürkay AKBAŞ Zehirlenmeye Genel YaklaşımHarun GÜNEŞEmine AYHAN AKMAN Anüri - HematüriSeçil AYDIN Anormal Uterin KanamaMeliha Miray ÇANTAL Acil DoğumDuygu LAFCIMurat BAŞCI Skrotal AğrıSümeyye KARABACAK Anorektal HastalıklarFerhat ÇAY Dermatolojik Acillere Yaklaşımİlkay CAN Döküntülü Bulaşıcı HastalıklarZekiye KANAT Ajite Hastaya YaklaşımMerve ŞAHİN CAN Çocuklarda ResüsitasyonÖzlem ÖZCANLI ÇAY Ateşli Çocuğa YaklaşımÖzlem ÖZCANLI ÇAY

Background: 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.