A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history.

ABSTRACT:

Objective

To facilitate patient disease subset and risk factor identification by constructing a pipeline which is generalizable, provides easily interpretable results, and allows replication by overcoming electronic health records (EHRs) batch effects.

Material and methods

We used 1872 billing codes in EHRs of 102 880 patients from 12 healthcare systems. Using tools borrowed from single-cell omics, we mitigated center-specific batch effects and performed clustering to identify patients with highly similar medical history patterns across the various centers. Our visualization method (PheSpec) depicts the phenotypic profile of clusters, applies a novel filtering of noninformative codes (Ranked Scope Pervasion), and indicates the most distinguishing features.

Results

We

SUBMITTER: Maurits MP 

PROVIDER: S-EPMC9122640 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature