Automated grouping of medical codes via multiview banded spectral clustering.

ABSTRACT:

Objective

With its increasingly widespread adoption, electronic health records (EHR) have enabled phenotypic information extraction at an unprecedented granularity and scale. However, often a medical concept (e.g. diagnosis, prescription, symptom) is described in various synonyms across different EHR systems, hindering data integration for signal enhancement and complicating dimensionality reduction for knowledge discovery. Despite existing ontologies and hierarchies, tremendous human effort is needed for curation and maintenance - a process that is both unscalable and susceptible to subjective biases. This paper aims to develop a data-driven approach to automate grouping medical terms into clinically relevant concepts by combining multiple up-to-date data sources in an unbiased ma

SUBMITTER: Zhang L 

PROVIDER: S-EPMC7261410 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature