DMCA
text-mining tools for data-mining tasks (2014)
BibTeX
@MISC{Jung14text-miningtools,
author = {Kenneth Jung and Paea Lependu and Srinivasan Iyer and Anna Bauer-mehren and Bethany Percha and Nigam H Shah},
title = {text-mining tools for data-mining tasks},
year = {2014}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Objective The trade-off between the speed and simplicity of dictionary-based term recognition and the richer linguistic information provided by more advanced natural language processing (NLP) is an area of active discussion in clinical in-formatics. In this paper, we quantify this trade-off among text processing systems that make different trade-offs between speed and linguistic understanding. We tested both types of systems in three clinical research tasks: phase IV safety profiling of a drug, learning adverse drug–drug interactions, and learning used-to-treat relationships between drugs and indications. Materials We first benchmarked the accuracy of the NCBO Annotator and REVEAL in a manually annotated, publically available dataset from the 2008 i2b2 Obesity Challenge. We then applied the NCBO Annotator and REVEAL to 9 million clinical notes from the Stanford Translational Research Integrated Database Environment (STRIDE) and used the result-ing data for three research tasks. Results There is no significant difference between using the NCBO Annotator and REVEAL in the results of the three research tasks when using large datasets. In one subtask, REVEAL achieved higher sensitivity with smaller datasets. Conclusions For a variety of tasks, employing simple term recognition methods instead of advanced NLP methods results in little or no impact on accuracy when using large datasets. Simpler dictionary-based methods have the advan-tage of scaling well to very large datasets. Promoting the use of simple, dictionary-based methods for population level analyses can advance adoption of NLP in practice. Key words: electronic health records, natural language processing, text mining
Keyphrases
ncbo annotator large datasets data-mining task text-mining tool research task significant difference linguistic understanding advanced natural language processing natural language processing clinical research task advanced nlp method result different trade-off result-ing data dictionary-based method active discussion linguistic information adverse drug drug interaction available dataset i2b2 obesity challenge text mining simple term recognition method phase iv safety profiling simpler dictionary-based method dictionary-based term recognition clinical in-formatics used-to-treat relationship key word population level analysis electronic health record clinical note
