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Predicting pulmonary embolism among hospitalized patients with machine learning algorithms
BACKGROUND: Pulmonary embolisms (PE) are life‐threatening medical events, and early identification of patients experiencing a PE is essential to optimizing patient outcomes. Current tools for risk stratification of PE patients are limited and unable to predict PE events before their occurrence. OBJE...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9052977/ https://www.ncbi.nlm.nih.gov/pubmed/35506114 http://dx.doi.org/10.1002/pul2.12013 |