Developing an Algorithm to Detect Early Childhood Obesity in Two Tertiary Pediatric Medical Centers

Abstract

SummaryObjective: 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). Introduction: 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. Data and Methods: 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. Results: 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. Conclusions: 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. Research Article

Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review

“…The former models use traditional statistical techniques, mainly logistic regression, [ 34 , 35 , 36 , 37 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ] although there are cases using linear regression [ 40 ], quantile regression [ 38 ], and ordinal logistic regression. [ 47 ] The ML models use a wide variety of ML methods: ANN [ 56 , 57 , 58 , 67 , 68 , 70 , 75 ], SVM [ 58 , 66 , 67 ], DT [ 58 , 64 , 65 , 67 , 68 , 69 , 70 , 73 ], NB [ 58 , 60 , 61 , 62 , 66 , 67 ], BN [ 58 , 65 , 67 , 76 ], LASSO [ 72 , 74 ], kNN [ 70 ], RF [ 59 , 65 , 68 , 72 ], GBM [ 72 , 77 ], and DL (RNN […”

“…A specially interesting situation from the point of view of predictor variables are the ML models that use EHR [ 65 , 66 , 71 , 72 , 77 ], since they appear as very powerful approaches to predict childhood/adolescent obesity by tapping from the large databases of medical records with many patients and extended sets of predictor variables, including measurements, drug prescriptions, conditions observed, and procedures requested. These are especially amenable of DL techniques of the RNN type, which are specialized in dealing with time serial data like this.…”

“…As described above, one case in the models presented here is that of Gupta et al [ 71 ], where they were able to deliver excellent predictions of BMI and obesity along the whole childhood and adolescence growth curve. Another interesting use of RNN in this framework would be the extraction of information from narrative data in the medical records by means of NLP; an example of predictor variables extracted through NLP is the work by Lingren et al [ 66 ].…”

“…In 2016, a paper by Lingren et al [ 66 ] was published aimed at the identification of putative cases of severe early childhood obesity from children 1–6 years old above the 99th BMI percentile, to separate them from those due to medications, pathologies, etc. The objective was to develop a cohort for further genotyping studies, in order to understand the genetic basis for severe early childhood obesity.…”

Suboptimal Clinical Documentation in Young Children with Severe Obesity at Tertiary Care Centers

“…As part of the eMERGE project, a validated electronic algorithm was established to identify cases of severe obesity using structured and nonstructured data fields captured in the EHR during clinical care [ 21 ]. BMI was calculated by the EHR systems from height (or length, if under the age of 2) and weight data recorded at the same visit by medical assistants and/or nurses during the course of routine clinical care.…”