title: Developing an Algorithm to Detect Early Childhood Obesity in Two Tertiary Pediatric Medical Centers.
abstract: The objective of this study is to develop an algorithm to accurately identify children with severe early onset childhood obesity (ages 1-5.99 years) using structured and unstructured data from the electronic health record (EHR).
  
  
  
  Childhood obesity increases risk factors for cardiovascular morbidity and vascular disease. Accurate definition of a high precision phenotype through a standardize tool is critical to the success of large-scale genomic studies and validating rare monogenic variants causing severe early onset obesity.
  
  
  
  Rule based and machine learning based algorithms were developed using structured and unstructured data from two EHR databases from Boston Children's Hospital (BCH) and Cincinnati Children's Hospital and Medical Center (CCHMC). Exclusion criteria including medications or comorbid diagnoses were defined. Machine learning algorithms were developed using cross-site training and testing in addition to experimenting with natural language processing features.
  
  
  
  Precision was emphasized for a high fidelity cohort. The rule-based algorithm performed the best overall, 0.895 (CCHMC) and 0.770 (BCH). The best feature set for machine learning employed Unified Medical Language System (UMLS) concept unique identifiers (CUIs), ICD-9 codes, and RxNorm codes.
  
  
  
  Detecting severe early childhood obesity is essential for the intervention potential in children at the highest long-term risk of developing comorbidities related to obesity and excluding patients with underlying pathological and non-syndromic causes of obesity assists in developing a high-precision cohort for genetic study. Further such phenotyping efforts inform future practical application in health care environments utilizing clinical decision support.
authors.affiliation: Todd Lingren, Cincinnati Children's Hospital Medical Center, Biomedical Informatics, 3333 Burnet Avenue, MLC 7024 Cincinnati, OH 45229-3039, Phone: 513-803-9032, Fax: 513-636-2056, Email: todd.lingren@cchmc.org.
authors.forename: Todd
authors.initials: T
authors.lastname: Lingren
authors.forename: Vidhu
authors.initials: V
authors.lastname: Thaker
authors.forename: Cassandra
authors.initials: C
authors.lastname: Brady
authors.forename: Bahram
authors.initials: B
authors.lastname: Namjou
authors.forename: Stephanie
authors.initials: S
authors.lastname: Kennebeck
authors.forename: Jonathan
authors.initials: J
authors.lastname: Bickel
authors.forename: Nandan
authors.initials: N
authors.lastname: Patibandla
authors.forename: Yizhao
authors.initials: Y
authors.lastname: Ni
authors.forename: Sara L
authors.initials: SL
authors.lastname: Van Driest
authors.forename: Lixin
authors.initials: L
authors.lastname: Chen
authors.forename: Ashton
authors.initials: A
authors.lastname: Roach
authors.forename: Beth
authors.initials: B
authors.lastname: Cobb
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Kirby
authors.forename: Josh
authors.initials: J
authors.lastname: Denny
authors.forename: Lisa
authors.initials: L
authors.lastname: Bailey-Davis
authors.forename: Marc S
authors.initials: MS
authors.lastname: Williams
authors.forename: Keith
authors.initials: K
authors.lastname: Marsolo
authors.forename: Imre
authors.initials: I
authors.lastname: Solti
authors.forename: Ingrid A
authors.initials: IA
authors.lastname: Holm
authors.forename: John
authors.initials: J
authors.lastname: Harley
authors.forename: Isaac S
authors.initials: IS
authors.lastname: Kohane
authors.forename: Guergana
authors.initials: G
authors.lastname: Savova
authors.forename: Nancy
authors.initials: N
authors.lastname: Crimmins
mesh_terms: D002648:Child; D002675:Child, Preschool; D015897:Comorbidity; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D007223:Infant; D000069550:Machine Learning; D008297:Male; D063766:Pediatric Obesity / Q000175:diagnosis* / Q000453:epidemiology; D063128:Tertiary Health Care
keywords: Electronic health record; algorithm; machine learning; obesity; phenotype