Deduplication in a massive clinical note dataset
Abstract
Duplication, whether exact or partial, is a common issue in many datasets. In clinical notes data, duplication (and near duplication) can arise for many reasons, such as the pervasive use of templates, copy-pasting, or notes being generated by automated procedures. A key challenge in removing such near duplicates is the size of such datasets; our own dataset consists of more than 10 million notes. To detect and correct such duplicates requires algorithms that both accurate and highly scalable. We describe a solution based on Minhashing with Locality Sensitive Hashing. In this paper, we present the theory behind this method and present a database-inspired approach to make the method scalable. We also present a clustering technique using disjoint sets to produce dense clusters, which speeds up our algorithm.
Cite
@article{arxiv.1704.05617,
title = {Deduplication in a massive clinical note dataset},
author = {Sanjeev Shenoy and Tsung-Ting Kuo and Rodney Gabriel and Julian McAuley and Chun-Nan Hsu},
journal= {arXiv preprint arXiv:1704.05617},
year = {2017}
}
Comments
Extended from the Master project report of Sanjeev Shenoy, Department of Computer Science and Engineering, University of California, San Diego. June 2016