Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data.

Campbell, Elizabeth A; Qian, Ting; Miller, Jeffrey M; Bass, Ellen J; Masino, Aaron J

Campbell, Elizabeth A; Qian, Ting; Miller, Jeffrey M; Bass, Ellen J; Masino, Aaron J.

Afiliação

Campbell EA; Department of Information Science, College of Computing and Informatics, Drexel University, Philadelphia, PA, USA.
Qian T; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Miller JM; Department of Psychology, Princeton University, Princeton, NJ, USA.
Bass EJ; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Masino AJ; Department of Information Science, College of Computing and Informatics, Drexel University, Philadelphia, PA, USA.

Int J Obes (Lond) ; 44(8): 1753-1765, 2020 08.

Article em En | MEDLINE | ID: mdl-32494036

Assuntos

Big Data; Obesidade Infantil/epidemiologia; Adolescente; Asma/epidemiologia; Estudos de Casos e Controles; Criança; Pré-Escolar; Registros Eletrônicos de Saúde; Feminino; Humanos; Incidência; Masculino; Philadelphia/epidemiologia; Estudos Retrospectivos; Rinite Alérgica/epidemiologia

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Child / Child, preschool / Female / Humans / Male País/Região como assunto: America do norte Idioma: En Ano de publicação: 2020 Tipo de documento: Article

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