An NLP Framework for the Extraction of Concept Measurements from Radiology and Pathology Notes
Year: 2024
Type: book-chapter
Abstract: Natural language processing (NLP) tools can automate the identification of cancer patients eligible for specific pathways. We developed and validated a cancer agnostic, rules-based NLP framework to extract the dimensions and measurements of several concepts from pathology and radiology reports. This framework was then efficiently and cost-effectively deployed to identify patients eligible for breast, lung, and prostate cancers clinical pathways.
DOI: 10.3233/shti231237
Authors: Annie Bowles, Cris Perez, Anil Vachani, Jennifer Steltz, Brent Rose, Alex K. Bryant, Hannah Eyre, Scott L. DuVall, Julie A. Lynch, Patrick R. Alba
Institutions: University of Utah, VA Salt Lake City Healthcare System, University of Pennsylvania, University of California San Diego, VA San Diego Healthcare System, United States Department of Veterans Affairs, University of Michigan, Veterans Health Administration, VA Ann Arbor Healthcare System, Ann Arbor VA Medical Center
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4391224119