Machine learning improves prediction of pulmonary thromboembolism and reduces unnecessary computed tomography scans in the emergency department

Abstract The diagnosis of pulmonary thromboembolism (PTE) remains challenging due to its nonspecific clinical signs and symptoms. This study aimed to develop a machine learning (ML) model to predict PTE in emergency department patients. We retrospectively analyzed 2,525 emergency department patients...

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Váldodahkkit:	Sung Hyun Yoon, Cheolho Kwon, Yeongho Choi, Hyung-Jun Kim, Jihang Kim, Young Hoon Kim
Materiálatiipa:	Artigo
Giella:	Inglês
Almmustuhtton:	Nature Portfolio 2026-01-01
Ráidu:	Scientific Reports
Fáttát:	Pulmonary embolism Computed tomography pulmonary angiography Clinical prediction rules Supervised machine learning
Liŋkkat:	https://doi.org/10.1038/s41598-025-34952-x
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