ArticleApplied clinical informatics2016
Developing an Algorithm to Detect Early Childhood Obesity in Two Tertiary Pediatric Medical Centers.
Article in Applied clinical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight.Journal of medical systems · 2023Pooled it
- Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies.Journal of biomedical semantics · 2020Pooled it
- Integration of fairness-awareness into clinical language processing models.Communications medicine · 2026Article
- Artificial Intelligence in Obesity Prevention.Healthcare (Basel, Switzerland) · 2025Review
- Classification of childhood obesity using longitudinal clinical body mass index and its validation.International journal of obesity (2005) · 2025Observational
- Development of Machine Learning-Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts.JMIR public health and surveillance · 2025Article
- Methods for identifying health status from routinely collected health data: An overview.Integrative medicine research · 2025Article
- Harnessing Artificial Intelligence in Obesity Research and Management: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Assessing the capture of sociodemographic information in electronic medical records to inform clinical decision making.PloS one · 2025Article
- External Validation of an Electronic Phenotyping Algorithm Detecting Attention to High Body Mass Index in Pediatric Primary Care.Applied clinical informatics · 2024Article
- An algorithm to identify patients aged 0-3 with rare genetic disorders.Orphanet journal of rare diseases · 2024Article
- Applications of artificial intelligence in the management of childhood obesity.Journal of family medicine and primary care · 2023Review
- Review
- Refining Clinical Phenotypes to Improve Clinical Decision Support and Reduce Alert Fatigue: A Feasibility Study.Applied clinical informatics · 2023Article
- Trends and opportunities in computable clinical phenotyping: A scoping review.Journal of biomedical informatics · 2023Article
- Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network.Scientific reports · 2023Article
- Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies.Journal of medical Internet research · 2022Article
- Obesity Prediction with EHR Data: A deep learning approach with interpretable elements.ACM transactions on computing for healthcare · 2022Article
- Efficacy of Emerging Technologies to Manage Childhood Obesity.Diabetes, metabolic syndrome and obesity : targets and therapy · 2022Review
- On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review.Frontiers in public health · 2022Review
Corrections and comments
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Authors and funding
23 authors.
Funding
Abstract
objectiveThe 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).
introductionChildhood 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. DATA AND
methodsRule 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.
resultsPrecision 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.
conclusionsDetecting 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.
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