Deep learning detects and visualizes bleeding events in electronic health records-Reference-Cited by-同舟云学术

Deep learning detects and visualizes bleeding events in electronic health records

Published:2021-05 Issue:4 Volume:5 Page:
ISSN:2475-0379
Container-title:Research and Practice in Thrombosis and Haemostasis
language:en
Short-container-title:Res Pract Thromb Haemost

Author:

Pedersen Jannik S.¹,Laursen Martin S.¹,Rajeeth Savarimuthu Thiusius¹,Hansen Rasmus Søgaard²,Alnor Anne Bryde²,Bjerre Kristian Voss²,Kjær Ina Mathilde³,Gils Charlotte²,Thorsen Anne‐Sofie Faarvang²,Andersen Eline Sandvig³,Nielsen Cathrine Brødsgaard⁴,Andersen Lou‐Ann Christensen⁵,Just Søren Andreas⁶,Vinholt Pernille Just²

Affiliation:

1. The Maersk Mc‐Kinney Moller Institute University of Southern Denmark Odense Denmark

2. Department of Clinical Biochemistry and Pharmacology Odense University Hospital Odense Denmark

3. Department of Clinical Biochemistry and Immunology Lillebaelt Hospital Denmark

4. Department of Gastroenterology Odense University Hospital Odense Denmark

5. Department of Ophthalmology Odense University Hospital Odense Denmark

6. Department of Medicine Odense University Hospital Svendborg Denmark

Publisher

Wiley

Subject

Hematology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/rth2.12505

Reference36 articles.

1. Factors at Admission Associated With Bleeding Risk in Medical Patients

2. Bleeding, mortality, and antiplatelet therapy: Results from the Clopidogrel for High Atherothrombotic Risk and Ischemic Stabilization, Management, and Avoidance (CHARISMA) trial

3. A Novel User-Friendly Score (HAS-BLED) To Assess 1-Year Risk of Major Bleeding in Patients With Atrial Fibrillation

4. ISTH/SSC bleeding assessment tool: a standardized questionnaire and a proposal for a new bleeding score for inherited bleeding disorders

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