TY - JOUR
T1 - TextHunter--A User Friendly Tool for Extracting Generic Concepts from Free Text in Clinical Research
AU - Jackson MSc, Richard G.
AU - Ball, Michael
AU - Patel, Rashmi
AU - Hayes, Richard D.
AU - Dobson, Richard J B
AU - Stewart, Robert
PY - 2014
Y1 - 2014
N2 - Observational research using data from electronic health records (EHR) is a rapidly growing area, which promises both increased sample size and data richness - therefore unprecedented study power. However, in many medical domains, large amounts of potentially valuable data are contained within the free text clinical narrative. Manually reviewing free text to obtain desired information is an inefficient use of researcher time and skill. Previous work has demonstrated the feasibility of applying Natural Language Processing (NLP) to extract information. However, in real world research environments, the demand for NLP skills outweighs supply, creating a bottleneck in the secondary exploitation of the EHR. To address this, we present TextHunter, a tool for the creation of training data, construction of concept extraction machine learning models and their application to documents. Using confidence thresholds to ensure high precision (>90%), we achieved recall measurements as high as 99% in real world use cases.
AB - Observational research using data from electronic health records (EHR) is a rapidly growing area, which promises both increased sample size and data richness - therefore unprecedented study power. However, in many medical domains, large amounts of potentially valuable data are contained within the free text clinical narrative. Manually reviewing free text to obtain desired information is an inefficient use of researcher time and skill. Previous work has demonstrated the feasibility of applying Natural Language Processing (NLP) to extract information. However, in real world research environments, the demand for NLP skills outweighs supply, creating a bottleneck in the secondary exploitation of the EHR. To address this, we present TextHunter, a tool for the creation of training data, construction of concept extraction machine learning models and their application to documents. Using confidence thresholds to ensure high precision (>90%), we achieved recall measurements as high as 99% in real world use cases.
UR - http://www.scopus.com/inward/record.url?scp=84964313512&partnerID=8YFLogxK
M3 - Article
C2 - 25954379
AN - SCOPUS:84964313512
SN - 1942-597X
VL - 2014
SP - 729
EP - 738
JO - Proceedings of the American Medical Informatics Association
JF - Proceedings of the American Medical Informatics Association
ER -