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Journal of Data Science


Scalable Community Extraction of Text Networks for Automated Grouping in Medical Databases
Volume 21, Issue 3 (2023): Special Issue: Advances in Network Data Science, pp. 470–489
Pub. online: 19 April 2022      Type: Statistical Data Science      Open accessOpen Access
Received
4 November 2021
Accepted
13 February 2022
Published
19 April 2022

Abstract

Networks are ubiquitous in today’s world. Community structure is a well-known feature of many empirical networks, and a lot of statistical methods have been developed for community detection. In this paper, we consider the problem of community extraction in text networks, which is greatly relevant in medical errors and patient safety databases. We adapt a well-known community extraction method to develop a scalable algorithm for extracting groups of similar documents in large text databases. The application of our method on a real-world patient safety report system demonstrates that the groups generated from community extraction are much more accurate than manual tagging by frontline workers.

Supplementary material

 Supplementary Material
Supplementary material online include R code for implementing the proposed method.

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Copyright
2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
community structure community extraction natural language processing patient safety

Funding
We acknowledge the support from the NIH R01 grant 1R01LM013309 from the National Library of Medicine.

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