Building a common pipeline for rule-based document classification.
Instance-based classification of clinical text is a widely used natural language processing task employed as a step for patient classification, document retrieval, or information extraction. Rule-based approaches rely on concept identification and context analysis in order to determine the appropriate class. We propose a five-step process that enables even small research teams to develop simple but powerful rule-based NLP systems by taking advantage of a common UIMA AS based pipeline for classification. Our proposed methodology coupled with the general-purpose solution provides researchers with access to the data locked in clinical text in cases of limited human resources and compact timelines.
VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
Olga V
OV
Patterson
Thomas
T
Ginter
Scott L
SL
DuVall
D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D004282:Documentation / Q000145:classification*; D057286:Electronic Health Records / Q000145:classification; D009323:Natural Language Processing; D012984:Software; D018875:Vocabulary, Controlled