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

Medicine and Health

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

E. A. Campbell, T. Qian, et al.

This groundbreaking study conducted by Elizabeth A. Campbell, Ting Qian, Jeffrey M. Miller, Ellen J. Bass, and Aaron J. Masino explores crucial temporal patterns linked to pediatric obesity using EHR data. It uncovers strong associations between pre-obesity conditions like asthma and allergic rhinitis, suggesting these could be early indicators of obesity.

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Playback language: English
Abstract
This study aimed to identify temporal condition patterns associated with pediatric obesity incidence using electronic health record (EHR) data from the Children's Hospital of Philadelphia. Researchers compared 49,694 obese patients with matched controls using the SPADE algorithm to analyze condition sequences before, during, and after the first obesity diagnosis. Asthma and allergic rhinitis showed strong associations with obesity, particularly before the diagnosis. Seven conditions (including ear, nose, and throat disorders and gastroenteritis) were exclusively diagnosed before obesity onset. These findings suggest potential early indicators of obesity, though further research is needed to determine causal relationships.
Publisher
Observational Health Data Sciences and Informatics
Published On
Jun 03, 2020
Authors
Elizabeth A. Campbell, Ting Qian, Jeffrey M. Miller, Ellen J. Bass, Aaron J. Masino
Tags
pediatric obesity
electronic health records
condition patterns
asthma
allergic rhinitis
early indicators
causal relationships

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