Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data
Background Electronic health records (EHRs) are potentially important components in addressing pediatric obesity in clinical settings and at the population level. This work aims to identify temporal condition patterns surrounding obesity incidence in a large pediatric population that may inform clin...
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Veröffentlicht in: | International Journal of Obesity 2020-08, Vol.44 (8), p.1753-1765 |
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Sprache: | eng |
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Zusammenfassung: | Background
Electronic health records (EHRs) are potentially important components in addressing pediatric obesity in clinical settings and at the population level. This work aims to identify temporal condition patterns surrounding obesity incidence in a large pediatric population that may inform clinical care and childhood obesity policy and prevention efforts.
Methods
EHR data from healthcare visits with an initial record of obesity incidence (index visit) from 2009 through 2016 at the Children’s Hospital of Philadelphia, and visits immediately before (pre-index) and after (post-index), were compared with a matched control population of patients with a healthy weight to characterize the prevalence of common diagnoses and condition trajectories. The study population consisted of 49,694 patients with pediatric obesity and their corresponding matched controls. The SPADE algorithm was used to identify common temporal condition patterns in the case population. McNemar’s test was used to assess the statistical significance of pattern prevalence differences between the case and control populations.
Results
SPADE identified 163 condition patterns that were present in at least 1% of cases; 80 were significantly more common among cases and 45 were significantly more common among controls (
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ISSN: | 0307-0565 1476-5497 1476-5497 |
DOI: | 10.1038/s41366-020-0614-7 |