Novel Pediatric Height Outlier Detection Methodology for Electronic Health Records via Machine Learning With Monotonic Bayesian Additive Regression Trees.

Objective: To create a new methodology that has a single simple rule to identify height outliers in the electronic health records (EHR) of children.

Methods: We constructed 2 independent cohorts of children 2 to 8 years old to train and validate a model predicting heights from age, gender, race and weight with monotonic Bayesian additive regression trees. The training cohort consisted of 1376 children where outliers were unknown.

A Systematic Review of Micronutrient Deficiencies in Pediatric Inflammatory Bowel Disease.

Background: This systematic review critically analyzes the current research on micronutrient deficiency in children with inflammatory bowel disease (IBD) and synthesizes these data to provide evidence-based guidelines for nutritional surveillance in this population.

Methods: We searched 5 databases (Ovid Medline, PubMed, Scopus, CINAHL, and Cochrane Library) for studies evaluating micronutrients in patients with IBD using the following inclusion criteria: 1) original research, 2) published 1996 or later; 3) published in English; 4) human subjects; and 5) containing pediatric data. Studies were reviewed and included based on the strength of research methods.