Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping.

ABSTRACT: Electronic Health Record (EHR) data, a rich source for biomedical research, have been successfully used to gain novel insight into a wide range of diseases. Despite its potential, EHR is currently underutilized for discovery research due to its major limitation in the lack of precise phenotype information. To overcome such difficulties, recent efforts have been devoted to developing supervised algorithms to accurately predict phenotypes based on relatively small training datasets with gold standard labels extracted via chart review. However, supervised methods typically require a sizable training set to yield generalizable algorithms, especially when the number of candidate features, p, is large. In this paper, we propose a semi-supervised (SS) EHR phenotyping method that borrows informati

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10653017 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature