aceMachine Learning Models for Suspected Pulmonary Embolism in Emergency Department Patients: A Multicentre Diagnostic Study
Pulmonary embolism (PE) is frequently suspected in emergency departments (EDs). Safely ruling out PE without additional testing could reduce imaging use and ED crowding. Machine learning (ML) may improve accuracy and efficiency.To evaluate ML models for ruling out PE without additional testing in ED patients with suspected PE, while maintaining a failure rate n = 2,400) and validation ( n = 2,638) datasets. ML models classified patients into low, intermediate, and high pre-test probability groups. In intermediat…
A two-step ML algorithm ruled out pulmonary embolism with a failure rate below the 2% criterion and imaging use similar to the 4PEPS rule, and it did so without any testing for some patients.
Evidence
- Peer-reviewedThrombosis and Haemostasis2026-10-08
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Truvace Impact Record TRV-2026-1739, v1: “Machine Learning Models for Suspected Pulmonary Embolism in Emergency Department Patients: A Multicentre Diagnostic Study.” Truvace, 2026-10-17. /record/TRV-2026-1739 (accessed at citation time). sha256 9ce90080ea74a278…
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