EHR-based phenotyping: Bulk learning and evaluation.

ABSTRACT: In data-driven phenotyping, a core computational task is to identify medical concepts and their variations from sources of electronic health records (EHR) to stratify phenotypic cohorts. A conventional analytic framework for phenotyping largely uses a manual knowledge engineering approach or a supervised learning approach where clinical cases are represented by variables encompassing diagnoses, medicinal treatments and laboratory tests, among others. In such a framework, tasks associated with feature engineering and data annotation remain a tedious and expensive exercise, resulting in poor scalability. In addition, certain clinical conditions, such as those that are rare and acute in nature, may never accumulate sufficient data over time, which poses a challenge to establishing accurate an

SUBMITTER: Chiu PH 

PROVIDER: S-EPMC5934756 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature