Development of a machine learning algorithm based on administrative claims data for identification of ED anaphylaxis patient visits.

Campbell, Ronna L; Alpern, Mollie L; Li, James T; Hagan, John B; Motosue, Megan; Mullan, Aidan F; Harper, Lauren S; Lohse, Christine M; Jeffery, Molly M

Campbell, Ronna L; Alpern, Mollie L; Li, James T; Hagan, John B; Motosue, Megan; Mullan, Aidan F; Harper, Lauren S; Lohse, Christine M; Jeffery, Molly M.

Afiliación

Campbell RL; Department of Emergency Medicine, Mayo Clinic, Rochester.
Alpern ML; Division of Allergic Diseases, Mayo Clinic, Rochester.
Li JT; Division of Allergic Diseases, Mayo Clinic, Rochester.
Hagan JB; Division of Allergic Diseases, Mayo Clinic, Rochester.
Motosue M; Division of Allergy and Immunology, Kaiser Permanente Honolulu Clinic, Honolulu.
Mullan AF; Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester.
Harper LS; Department of Emergency Medicine, Mayo Clinic, Rochester.
Lohse CM; Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester.
Jeffery MM; Department of Emergency Medicine, Mayo Clinic, Rochester.

J Allergy Clin Immunol Glob ; 2(1): 61-68, 2023 Feb.

Article en En | MEDLINE | ID: mdl-37780106

Palabras clave

Anaphylaxis; emergency department; epidemiology; machine learning

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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: J Allergy Clin Immunol Glob Año: 2023 Tipo del documento: Article

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