An efficient machine learning framework to identify important clinical features associated with pulmonary embolism.
A misdiagnosis of pulmonary embolism (PE) can have severe consequences such as disability or death. It's crucial to accurately identify key clinical features of PE in clinical practice to promptly identify potential PE patients who may present asymptomatically, and to prevent misdiagnosing PE a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0292185 |