Transitive Sequencing Medical Records for Mining Predictive and Interpretable Temporal Representations.

ABSTRACT: Electronic health records (EHRs) contain important temporal information about the progression of disease and treatment outcomes. This paper proposes a transitive sequencing approach for constructing temporal representations from EHR observations for downstream machine learning. Using clinical data from a cohort of patients with congestive heart failure, we mined temporal representations by transitive sequencing of EHR medication and diagnosis records for classification and prediction tasks. We compared the classification and prediction performances of the transitive sequential representations (bag-of-sequences approach) with the conventional approach of using aggregated vectors of EHR data (aggregated vector representation) across different classifiers. We found that the transitive sequent

SUBMITTER: Estiri H 

PROVIDER: S-EPMC7301790 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature