Combining knowledge- and data-driven methods for de-identification of clinical narratives.

ABSTRACT: A recent promise to access unstructured clinical data from electronic health records on large-scale has revitalized the interest in automated de-identification of clinical notes, which includes the identification of mentions of Protected Health Information (PHI). We describe the methods developed and evaluated as part of the i2b2/UTHealth 2014 challenge to identify PHI defined by 25 entity types in longitudinal clinical narratives. Our approach combines knowledge-driven (dictionaries and rules) and data-driven (machine learning) methods with a large range of features to address de-identification of specific named entities. In addition, we have devised a two-pass recognition approach that creates a patient-specific run-time dictionary from the PHI entities identified in the first step with

SUBMITTER: Dehghan A 

PROVIDER: S-EPMC4976126 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature