Characterizing clinical pediatric obesity subtypes using electronic health record data.

Elizabeth A Campbell^1,2, Mitchell G Maltenfort², Justine Shults², Christopher B Forrest^2,3, Aaron J Masino⁴

¹Department of Information Science, College of Computing & Informatics, Drexel University, Philadelphia, Pennsylvania, United States of America.

²Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.

³University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America.

⁴AiCure, New York, New York, United States of America.

PubMed PMC DOI PDF

Abstract

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 clinically similar patients. In a previous study, the sequence mining algorithm, SPADE was implemented…

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