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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主要な著者:	Sung Hyun Yoon, Cheolho Kwon, Yeongho Choi, Hyung-Jun Kim, Jihang Kim, Young Hoon Kim
フォーマット:	Artigo
言語:	Inglês
出版事項:	Nature Portfolio 2026-01-01
シリーズ:	Scientific Reports
主題:	Pulmonary embolism Computed tomography pulmonary angiography Clinical prediction rules Supervised machine learning
オンライン･アクセス:	https://doi.org/10.1038/s41598-025-34952-x
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