High-throughput phenotyping with temporal sequences.

ABSTRACT:

Objective

High-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs.

Materials and methods

We develop a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (aggregated vector representation), the standard sequential patterns (sequential pattern mining), the transitive sequential patterns (transitive sequential pattern mining), and 2 hybrid classes. Using EHR data on

SUBMITTER: Estiri H 

PROVIDER: S-EPMC7973443 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature