  Laboratory test results are an important and generally high dimensional
component of a patient's Electronic Health Record (EHR). We train embedding
representations (via Word2Vec and GloVe) for LOINC codes of laboratory tests
from the EHRs of about 80,000 patients at a cancer center. To include
information about lab test outcomes, we also train embeddings on the
concatenation of a LOINC code with a symbol indicating normality or abnormality
of the result. We observe several clinically meaningful similarities among
LOINC embeddings trained over our data. For the embeddings of the concatenation
of LOINCs with abnormality codes, we evaluate the performance for mortality
prediction tasks and the ability to preserve ordinality properties: i.e. a lab
test with normal outcome should be more similar to an abnormal one than to the
a very abnormal one.
