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arXiv · 1704.05617

Deduplication in a massive clinical note dataset

Sanjeev Shenoy·Tsung-Ting Kuo·Rodney Gabriel·Julian McAuley·Chun-Nan Hsu
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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.

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Sanjeev Shenoy, Tsung-Ting Kuo, Rodney Gabriel, Julian McAuley, Chun-Nan Hsu. 2017-04-19. Deduplication in a massive clinical note dataset. https://arxiv.org/abs/1704.05617

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