Work

An information extraction framework for cohort identification using electronic health records.

Year: 2013

Type: article

Abstract: Information extraction (IE), a natural language processing (NLP) task that automatically extracts structured or semi-structured information from free text, has become popular in the clinical domain for supporting automated systems at point-of-care and enabling secondary use of electronic health records (EHRs) for clinical and translational research. However, a high performance IE system can be very challenging to construct due to the complexity and dynamic nature of human language. In this paper, we report an IE framework for cohort identification using EHRs that is a knowledge-driven framework developed under the Unstructured Information Management Architecture (UIMA). A system to extract specific information can be developed by subject matter experts through expert knowledge engineering of the externalized knowledge resources used in the framework.

Source: Europe PMC (PubMed Central)

Authors: Hongfang Liu, Suzette J Bielinski, Sunghwan Sohn, Sean Murphy, Kavishwar B Wagholikar, Siddhartha R Jonnalagadda, K E Ravikumar, Stephen T Wu, Iftikhar J Kullo, Christopher G Chute

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

Open Access status: green

Funders: National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Institute of General Medical Sciences

Awards: EHR-based Genomic Discovery and Implementation, Natural language processing for clinical and translational research, Heart Failure in the Community

OpenAlex ID: W86609720

FWCI: 3.172

Cites: 22

Cited by: 162

Related to: 10