Cargando…
Characterizing clinical pediatric obesity subtypes using electronic health record data
In this work, we present a study of electronic health record (EHR) data that aims to identify pediatric obesity clinical subtypes. Specifically, we examine whether certain temporal condition patterns associated with childhood obesity incidence tend to cluster together to characterize subtypes of cli...
Autores principales: | Campbell, Elizabeth A., Maltenfort, Mitchell G., Shults, Justine, Forrest, Christopher B., Masino, Aaron J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931247/ https://www.ncbi.nlm.nih.gov/pubmed/36812554 http://dx.doi.org/10.1371/journal.pdig.0000073 |
Ejemplares similares
-
Temporal condition pattern mining in large, sparse electronic health record data: A case study in characterizing pediatric asthma
por: Campbell, Elizabeth A, et al.
Publicado: (2020) -
Electronic health records identify timely trends in childhood mental health conditions
por: Elia, Josephine, et al.
Publicado: (2023) -
Prediction of 30-day pediatric unplanned hospitalizations using the Johns Hopkins Adjusted Clinical Groups risk adjustment system
por: Maltenfort, Mitchell G., et al.
Publicado: (2019) -
Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data
por: Campbell, Elizabeth A., et al.
Publicado: (2020) -
Derivation of paediatric blood pressure percentiles from electronic health records
por: Mitsnefes, Mark M., et al.
Publicado: (2023)