Imputing Growth Snapshot Similarity in Early Childhood Development: A Tensor Decomposition Approach

In this paper, we discuss a tensor decomposition method for imputing similarity scores between individual clinical pictures at predefined patient age intervals in order to construct a dynamic similarity network of patients with respect to early childhood anthropomorphic development. The method leverages Canonical Polyadic Decomposition (or PARAFAC) to compute missing Euclidean similarity scores between pairwise growth pictures, made up of height and weight measurements. We construct a tensor made up of serial affinity matrices to model how the similarities between different patients change over different trajectory snapshots. We intend to use this method to aid Un Kilo de Ayuda (UKA), a non-governmental organization located in Mexico that is made up of facilitators seeking to identify children at risk for malnutrition and suboptimal development. This tensor completion strategy will assist UKA with determining pairs of children with similar clinical pictures, so that they can better assist with selecting treatment strategies and ultimately build better programs tailored to specific families’ needs.

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