Development and Validation of a Deep Learning Model for Detection of Allergic Reactions Using Safety Event Reports Across Hospitals-Reference-Cited by-同舟云学术

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Development and Validation of a Deep Learning Model for Detection of Allergic Reactions Using Safety Event Reports Across Hospitals

Published:2020-11-16 Issue:11 Volume:3 Page:e2022836
ISSN:2574-3805
Container-title:JAMA Network Open
language:en
Short-container-title:JAMA Netw Open

Author:

Yang Jie¹²,Wang Liqin¹²,Phadke Neelam A.²³,Wickner Paige G.²⁴,Mancini Christian M.²³,Blumenthal Kimberly G.²³,Zhou Li¹²

Affiliation:

1. Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, Massachusetts

2. Harvard Medical School, Boston, Massachusetts

3. Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Boston

4. Division of Allergy and Clinical Immunology, Brigham and Women’s Hospital, Boston, Massachusetts

Publisher

American Medical Association (AMA)

Subject

General Medicine

Link

https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2772992/yang_2020_oi_200761_1604932459.00515.pdf

Reference42 articles.

1. Prevalence of challenge-proven IgE-mediated food allergy using population-based sampling and predetermined challenge criteria in infants.;Osborne;J Allergy Clin Immunol,2011

2. Drug allergies documented in electronic health records of a large healthcare system.;Zhou;Allergy,2016

3. Prevalence of food allergies and intolerances documented in electronic health records.;Acker;J Allergy Clin Immunol,2017

4. Adverse and hypersensitivity reactions to prescription nonsteroidal anti-inflammatory agents in a large health care system.;Blumenthal;J Allergy Clin Immunol Pract,2017

5. Incidence of adverse drug events and potential adverse drug events. Implications for prevention.;Bates;JAMA,1995

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