Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models.

Hurst, Jillian H; Zhao, Congwen; Hostetler, Haley P; Ghiasi Gorveh, Mohsen; Lang, Jason E; Goldstein, Benjamin A

Hurst, Jillian H; Zhao, Congwen; Hostetler, Haley P; Ghiasi Gorveh, Mohsen; Lang, Jason E; Goldstein, Benjamin A.

Afiliación

Hurst JH; Department of Pediatrics, Division of Infectious Diseases, Duke University School of Medicine, Durham, NC, USA.
Zhao C; Department of Pediatrics, Children's Health and Discovery Initiative, Duke University School of Medicine, Durham, NC, USA.
Hostetler HP; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Ghiasi Gorveh M; Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University School of Medicine, Durham, NC, USA.
Lang JE; Duke Clinical Research Institute, Duke University, Durham, NC, USA.
Goldstein BA; Department of Pediatrics, Division of Pulmonary and Sleep Medicine, Duke University School of Medicine, Durham, NC, USA.

BMC Med Inform Decis Mak ; 22(1): 108, 2022 04 22.

Article en En | MEDLINE | ID: mdl-35459216

Asunto(s)

Asma; Aprendizaje Automático; Niño; Registros Electrónicos de Salud; Humanos; Curva ROC; Estudios Retrospectivos

Palabras clave

Asthma; Environmental data; Machine learning; Pediatrics; Predictive modeling

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Asma / Aprendizaje Automático Tipo de estudio: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Child / Humans Idioma: En Revista: BMC Med Inform Decis Mak Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

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