Enhancing childhood nutrition data with open biomedical ontologies

This poster reports our ongoing work using open biomedical ontologies to organize and analyze a diverse longitudinal dataset with elements pertaining to childhood nutrition and development. This work is part of a larger research project that seeks to advance understanding of dietary influences on psychological and neuropsychophysiological development and function in children. Our set of 124 data elements for 600 patients includes: infant diet groups (breast milk, soy-based formula, milk-based formula), sex, size measurements (weight, length, head) at birth and at regular intervals, maternal and paternal IQ, parental socioeconomic status at yearly intervals, and a variety of mental, language, and motor developmental scores at regular intervals. Our analysis of these data aims to identify discrete phenotypic cohorts and predict cognitive outcome measures over time. Exploratory machine learning analyses of the raw data highlighted the need for additional feature engineering to extract meaningful information. Toward this end we have deployed biomedical ontologies and semantic representations that capture connections among these data. We construct and use explicit representations of background knowledge from relevant domain ontologies and have developed an application ontology with a small number of unique terms.