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...

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Detalles Bibliográficos
Autores principales: Ryan, Logan, Maharjan, Jenish, Mataraso, Samson, Barnes, Gina, Hoffman, Jana, Mao, Qingqing, Calvert, Jacob, Das, Ritankar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Research Articles
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

Internet

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

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