Temporal phenotyping of medically complex children via PARAFAC2 tensor factorization.

Affiliation

Georgia Institute of Technology, United States. Electronic address: [Email]

Abstract

Our aim is to extract clinically-meaningful phenotypes from longitudinal electronic health records (EHRs) of medically-complex children. This is a fragile set of patients consuming a disproportionate amount of pediatric care resources but who often end up with sub-optimal clinical outcome. The rise in available electronic health records (EHRs) provide a rich data source that can be used to disentangle their complex clinical conditions into concise, clinically-meaningful groups of characteristics. We aim at identifying those phenotypes and their temporal evolution in a scalable, computational manner, which avoids the time-consuming manual chart review.

Keywords

Computational phenotyping,Temporal phenotyping,Tensor analysis,

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