Paper Info
Title
Taming Big Data: An Information Extraction Strategy for Large Clinical Text Corpora.
Abstract
Concepts of interest for clinical and research purposes are not uniformly distributed in clinical text available in electronic medical records. The purpose of our study was to identify filtering techniques to select 'high yield' documents for increased efficacy and throughput. Using two large corpora of clinical text, we demonstrate the identification of 'high yield' document sets in two unrelated domains: homelessness and indwelling urinary catheters. For homelessness, the high yield set includes homeless program and social work notes. For urinary catheters, concepts were more prevalent in notes from hospitalized patients; nursing notes accounted for a majority of the high yield set. This filtering will enable customization and refining of information extraction pipelines to facilitate extraction of relevant concepts for clinical decision support and other uses.
Year
DOI
Venue
2015
10.3233/978-1-61499-538-8-175
Studies in Health Technology and Informatics
Keywords
Field
DocType
Big data,natural language processing,information extraction
Information retrieval,Computer science,Text corpus,Information extraction,Artificial intelligence,Natural language processing,Big data
Conference
Volume
ISSN
Citations 
213
0926-9630
0
PageRank 
References 
Authors
0.34
1
7
Name
Order
Citations
PageRank
Adi Gundlapalli14714.74
Guy Divita265.48
Marjorie Carter385.52
Andrew Redd4116.59
Matthew H. Samore514326.07
kalpana gupta600.68
Barbara W. Trautner732.06