Using machine learning to improve anaphylaxis case identification in medical claims data.

Kural, Kamil Can; Mazo, Ilya; Walderhaug, Mark; Santana-Quintero, Luis; Karagiannis, Konstantinos; Thompson, Elaine E; Kelman, Jeffrey A; Goud, Ravi

Kural, Kamil Can; Mazo, Ilya; Walderhaug, Mark; Santana-Quintero, Luis; Karagiannis, Konstantinos; Thompson, Elaine E; Kelman, Jeffrey A; Goud, Ravi.

Afiliação

Kural KC; Center for Biologics Evaluation and Research (CBER), Food and Drug Administration, Silver Spring, MD 20993, United States.
Mazo I; School of Systems Biology, George Mason University, Manassas, VA 20110, United States.
Walderhaug M; Center for Biologics Evaluation and Research (CBER), Food and Drug Administration, Silver Spring, MD 20993, United States.
Santana-Quintero L; Center for Biologics Evaluation and Research (CBER), Food and Drug Administration, Silver Spring, MD 20993, United States.
Karagiannis K; Center for Biologics Evaluation and Research (CBER), Food and Drug Administration, Silver Spring, MD 20993, United States.
Thompson EE; Center for Biologics Evaluation and Research (CBER), Food and Drug Administration, Silver Spring, MD 20993, United States.
Kelman JA; Center for Biologics Evaluation and Research (CBER), Food and Drug Administration, Silver Spring, MD 20993, United States.
Goud R; Centers for Medicare & Medicaid Services, Washington, DC 20001, United States.

JAMIA Open ; 6(4): ooad090, 2023 Dec.

Article em En | MEDLINE | ID: mdl-37900974

Palavras-chave

Centers for Medicare and Medicaid Services; allergy; anaphylaxis; electronic health records; machine learning; public health

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: JAMIA Open Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

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