  It has been recently shown that sparse, nonnegative tensor factorization of
multi-modal electronic health record data is a promising approach to
high-throughput computational phenotyping. However, such approaches typically
do not leverage available domain knowledge while extracting the phenotypes;
hence, some of the suggested phenotypes may not map well to clinical concepts
or may be very similar to other suggested phenotypes. To address these issues,
we present a novel, automatic approach called PIVETed-Granite that mines
existing biomedical literature (PubMed) to obtain cannot-link constraints that
are then used as side-information during a tensor-factorization based
computational phenotyping process. The resulting improvements are clearly
observed in experiments using a large dataset from VUMC to identify phenotypes
for hypertensive patients.
