Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data.

ABSTRACT: The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative networ

SUBMITTER: Hong C 

PROVIDER: S-EPMC8551205 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature