Abstract
Manual extraction from electronic health records (EHRs) is currently the standard approach for accessing real-world healthcare data but can be time consuming and challenging to maintain over time. Automated data extraction using natural language processing (NLP) is emerging as a viable method of data extraction from structured and unstructured fields of EHRs. While speed of NLP-based data extraction is established, some question the validity of the extracted data. This study compares the accuracy of, and concordance between, manual and NLP-extracted data from EHRs of patients with advanced lung cancer (aLC).
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
