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Networks are ubiquitous in today's world. Community structure is a well-knownfeature of many empirical networks, and a lot of statistical methods have beendeveloped for community detection. In this paper, we consider the problem ofcommunity structure in text networks,which is greatly relevant in medicalerrors and patient safety databases. We adapt a well-known community extractionmethod to develop a scalable algorithm for community extraction in large textdatabases. The application of our method on a real-world patient safety reportdatabase demonstrates that the groups generated from community extraction aremuch more accurate than manual tagging by frontline workers.

Scalable Community Extraction of Text Networks for Automated Grouping in Medical Databases