A better performing algorithm for identification of implausible growth data from longitudinal pediatric medical records.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
06 Aug 2024
06 Aug 2024
Historique:
received:
07
02
2024
accepted:
01
08
2024
medline:
7
8
2024
pubmed:
7
8
2024
entrez:
6
8
2024
Statut:
epublish
Résumé
Tracking trajectories of body size in children provides insight into chronic disease risk. One measure of pediatric body size is body mass index (BMI), a function of height and weight. Errors in measuring height or weight may lead to incorrect assessment of BMI. Yet childhood measures of height and weight extracted from electronic medical records often include values which seem biologically implausible in the context of a growth trajectory. Removing biologically implausible values reduces noise in the data, and thus increases the ease of modeling associations between exposures and childhood BMI trajectories, or between childhood BMI trajectories and subsequent health conditions. We developed open-source algorithms (available on github) for detecting and removing biologically implausible values in pediatric trajectories of height and weight. A Monte Carlo simulation experiment compared the sensitivity, specificity and speed of our algorithms to three published algorithms. The comparator algorithms were selected because they used trajectory information, had open-source code, and had published verification studies. Simulation inputs were derived from longitudinal epidemiological cohorts. Our algorithms had higher specificity, with similar sensitivity and speed, when compared to the three published algorithms. The results suggest that our algorithms should be adopted for cleaning longitudinal pediatric growth data.
Identifiants
pubmed: 39107468
doi: 10.1038/s41598-024-69161-5
pii: 10.1038/s41598-024-69161-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
18276Subventions
Organisme : NIGMS NIH HHS
ID : 5R01GM121081-08
Pays : United States
Organisme : NIH HHS
ID : R01DK068001
Pays : United States
Informations de copyright
© 2024. The Author(s).
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