Deep learning detects and visualizes bleeding events in electronic health records

BACKGROUND: Bleeding is associated with a significantly increased morbidity and mortality. Bleeding events are often described in the unstructured text of electronic health records, which makes them difficult to identify by manual inspection. OBJECTIVES: To develop a deep learning model that detects...

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Detalles Bibliográficos
Autores principales: Pedersen, Jannik S., Laursen, Martin S., Rajeeth Savarimuthu, Thiusius, Hansen, Rasmus Søgaard, Alnor, Anne Bryde, Bjerre, Kristian Voss, Kjær, Ina Mathilde, Gils, Charlotte, Thorsen, Anne‐Sofie Faarvang, Andersen, Eline Sandvig, Nielsen, Cathrine Brødsgaard, Andersen, Lou‐Ann Christensen, Just, Søren Andreas, Vinholt, Pernille Just
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8114029/
https://www.ncbi.nlm.nih.gov/pubmed/34013150
http://dx.doi.org/10.1002/rth2.12505

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8114029/
https://www.ncbi.nlm.nih.gov/pubmed/34013150
http://dx.doi.org/10.1002/rth2.12505

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