Medical Concept Representation Learning from Multi-source Data.

ABSTRACT: Representing words as low dimensional vectors is very useful in many natural language processing tasks. This idea has been extended to medical domain where medical codes listed in medical claims are represented as vectors to facilitate exploratory analysis and predictive modeling. However, depending on a type of a medical provider, medical claims can use medical codes from different ontologies or from a combination of ontologies, which complicates learning of the representations. To be able to properly utilize such multi-source medical claim data, we propose an approach that represents medical codes from different ontologies in the same vector space. We first modify the Pointwise Mutual Information (PMI) measure of similarity between the codes. We then develop a new negative sampling metho

SUBMITTER: Bai T 

PROVIDER: S-EPMC7047512 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature