Title: CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data Model,
Abstract: Background: Widespread adoption of electronic health records (EHRs) has
enabled secondary use of EHR data for clinical research and healthcare
delivery. Natural language processing (NLP) techniques have shown promise in
their capability to extract the embedded information in unstructured clinical
data, and information retrieval (IR) techniques provide flexible and scalable
solutions that can augment the NLP systems for retrieving and ranking relevant
records. Methods: In this paper, we present the implementation of Cohort
Retrieval Enhanced by Analysis of Text from EHRs (CREATE), a cohort retrieval
system that can execute textual cohort selection queries on both structured and
unstructured EHR data. CREATE is a proof-of-concept system that leverages a
combination of structured queries and IR techniques on NLP results to improve
cohort retrieval performance while adopting the Observational Medical Outcomes
Partnership (OMOP) Common Data Model (CDM) to enhance model portability. The
NLP component empowered by cTAKES is used to extract CDM concepts from textual
queries. We design a hierarchical index in Elasticsearch to support CDM concept
search utilizing IR techniques and frameworks. Results: Our case study on 5
cohort identification queries evaluated using the IR metric, P@5 (Precision at
5) at both the patient-level and document-level, demonstrates that CREATE
achieves an average P@5 of 0.90, which outperforms systems using only
structured data or only unstructured data with average P@5s of 0.54 and 0.74,
respectively.