Improving Patient Cohort Identification Using Natural Language Processing

Retrieving information from structured data tables in a large database may be performed with little to no difficulty, but structured data may not always contain all that is needed to retrieve accurate information compared to narratives from clinical notes. The large volume of clinical notes, however, requires special processing to access the information contained in their unstructured format. In this case study, we present a comparison of two techniques (structured data extraction and natural language processing) and we evaluate their utility in identifying a specific patient cohort from a large clinical database.