v3NLP Framework: Tools to Build Applications for Extracting Concepts from Clinical Text.
BACKGROUND
Substantial amounts of clinically significant information are contained only within the narrative of the clinical notes in electronic medical records. The v3NLP Framework is a set of "best-of-breed" functionalities developed to transform this information into structured data for use in quality improvement, research, population health surveillance, and decision support.


BACKGROUND
MetaMap, cTAKES and similar well-known natural language processing (NLP) tools do not have sufficient scalability out of the box. The v3NLP Framework evolved out of the necessity to scale-up these tools up and provide a framework to customize and tune techniques that fit a variety of tasks, including document classification, tuned concept extraction for specific conditions, patient classification, and information retrieval.


METHODS
Beyond scalability, several v3NLP Framework-developed projects have been efficacy tested and benchmarked. While v3NLP Framework includes annotators, pipelines and applications, its functionalities enable developers to create novel annotators and to place annotators into pipelines and scaled applications.


CONCLUSIONS
The v3NLP Framework has been successfully utilized in many projects including general concept extraction, risk factors for homelessness among veterans, and identification of mentions of the presence of an indwelling urinary catheter. Projects as diverse as predicting colonization with methicillin-resistant Staphylococcus aureus and extracting references to military sexual trauma are being built using v3NLP Framework components.


CONCLUSIONS
The v3NLP Framework is a set of functionalities and components that provide Java developers with the ability to create novel annotators and to place those annotators into pipelines and applications to extract concepts from clinical text. There are scale-up and scale-out functionalities to process large numbers of records.
VA Salt Lake City Health Care System and University of Utah School of Medicine.
Guy
G
Divita
VA Salt Lake City Health Care System and University of Utah School of Medicine.
Marjorie E
ME
Carter
VA Salt Lake City Health Care System and University of Utah School of Medicine.
Le-Thuy
LT
Tran
VA Salt Lake City Health Care System and University of Utah School of Medicine.
Doug
D
Redd
VA Salt Lake City Health Care System and University of Utah School of Medicine.
Qing T
QT
Zeng
VA Salt Lake City Health Care System and University of Utah School of Medicine.
Scott
S
Duvall
VA Salt Lake City Health Care System and University of Utah School of Medicine.
Matthew H
MH
Samore
VA Salt Lake City Health Care System and University of Utah School of Medicine.
Adi V
AV
Gundlapalli
Natural Language Processing
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