Developing an Algorithm to Detect Early Childhood Obesity in Two Tertiary Pediatric Medical Centers
| Autor: | Jonathan Bickel, Cassandra Brady, Sara L. Van Driest, Keith Marsolo, John B. Harley, Ingrid A. Holm, Lixin Chen, Imre Solti, Bahram Namjou, Beth L. Cobb, Jacqueline Kirby, Yizhao Ni, Nancy A. Crimmins, Stephanie Kennebeck, Josh C. Denny, Todd Lingren, Lisa Bailey-Davis, Nandan Patibandla, Guergana Savova, Marc S. Williams, Ashton Roach, Vidhu V Thaker, Isaac S. Kohane |
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| Rok vydání: | 2016 |
| Předmět: |
Male
0301 basic medicine Pediatric Obesity Health Informatics Comorbidity Clinical decision support system Childhood obesity Machine Learning 03 medical and health sciences 0302 clinical medicine Health Information Management Health care medicine Humans 030212 general & internal medicine Early childhood Medical diagnosis Child Tertiary Healthcare business.industry Unified Medical Language System Infant medicine.disease Computer Science Applications Early Diagnosis 030104 developmental biology Child Preschool Cohort Female business RxNorm Algorithm Research Article |
| Zdroj: | Applied Clinical Informatics. :693-706 |
| ISSN: | 1869-0327 |
| Popis: | SummaryThe 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.Citation: Lingren T, Thaker V, Brady C, Namjou B, Kennebeck S, Bickel J, Patibandla N, Ni Y, Van Driest SL, Chen L, Roach A, Cobb B, Kirby J, Denny J, Bailey-Davis L, Williams MS, Marsolo K, Solti I, Holm IA, Harley J, Kohane IS, Savova G, Crimmins N. Developing an algorithm to detect early childhood obesity in two tertiary pediatric medical centers. |
| Databáze: | OpenAIRE |
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