Deep learning detects and visualizes bleeding events in electronic health records

Deep learning detects and visualizes bleeding events in electronic health records

Abstract 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...

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Bibliographic Details
Main Authors: Jannik S. Pedersen, Martin S. Laursen, Thiusius Rajeeth Savarimuthu, Rasmus Søgaard Hansen, Anne Bryde Alnor, Kristian Voss Bjerre, Ina Mathilde Kjær, Charlotte Gils, Anne‐Sofie Faarvang Thorsen, Eline Sandvig Andersen, Cathrine Brødsgaard Nielsen, Lou‐Ann Christensen Andersen, Søren Andreas Just, Pernille Just Vinholt
Format: Article
Language:English
Published: Wiley 2021-05-01
Series:Research and Practice in Thrombosis and Haemostasis
Subjects:
decision support systems (clinical)
deep learning
electronic health record
hemorrhage
international classification of diseases
machine learning
Online Access:https://doi.org/10.1002/rth2.12505

Internet

https://doi.org/10.1002/rth2.12505

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