Obtaining Knowledge in Pathology Reports Through a Natural Language Processing Approach With Classification, Named-Entity Recognition, and Relation-Extraction Heuristics.
Journal:
JCO clinical cancer informatics
Published Date:
Aug 1, 2019
Abstract
PURPOSE: Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology notes, which complicate data abstraction. Our motivation is to develop a natural language processing method that dynamically identifies existing pathology specimen elements necessary for locating specimens for future use in a manner that can be re-implemented by other institutions.
Authors
Keywords
Adolescent
Adult
Aged
Aged, 80 and over
Algorithms
Electronic Health Records
Female
Heuristics
Humans
Machine Learning
Male
Medical Informatics
Middle Aged
Natural Language Processing
Neoplasm Staging
Neoplasms
Pathology, Molecular
Research Report
Software
User-Computer Interface
Workflow
Young Adult