aceExplainable machine learning for early prediction and anatomical classification of pulmonary embolism in the emergency department
Pulmonary thromboembolism (PTE) is a life‑threatening condition that requires prompt and accurate evaluation in the emergency department (ED). Standardized clinical scoring systems, including the Wells and revised Geneva scores, form the cornerstone of initial risk stratification but have limited specificity, leading to unnecessary D‑dimer testing and frequent overuse of CT pulmonary angiography (CTPA). This study aimed to develop explainable machine‑learning (XML) models as a complementary decision‑support laye…
Explainable ML models used after Wells/Geneva triage improved early PTE prediction in ED patients, with Extra Trees reaching 0.82 accuracy and 0.83 AUC.
Anatomical classification performance was modest and cohort was restricted to intermediate- to high-probability ED patients with low confirmation rate.
Evidence
- Peer-reviewedBMC Emergency Medicine2026-07-17
How should this claim be treated?
Truvace Impact Record TRV-2026-0267, v1: “Explainable machine learning for early prediction and anatomical classification of pulmonary embolism in the emergency department.” Truvace, 2026-07-19. /record/TRV-2026-0267 (accessed at citation time). sha256 85663fd06e7ec5f4…
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