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Journal of Digital Imaging

, Volume 29, Issue 1, pp 59–62 | Cite as

Unsupervised Topic Modeling in a Large Free Text Radiology Report Repository

  • Saeed Hassanpour
  • Curtis P. Langlotz
Article

Abstract

Radiology report narrative contains a large amount of information about the patient’s health and the radiologist’s interpretation of medical findings. Most of this critical information is entered in free text format, even when structured radiology report templates are used. The radiology report narrative varies in use of terminology and language among different radiologists and organizations. The free text format and the subtlety and variations of natural language hinder the extraction of reusable information from radiology reports for decision support, quality improvement, and biomedical research. Therefore, as the first step to organize and extract the information content in a large multi-institutional free text radiology report repository, we have designed and developed an unsupervised machine learning approach to capture the main concepts in a radiology report repository and partition the reports based on their main foci. In this approach, radiology reports are modeled in a vector space and compared to each other through a cosine similarity measure. This similarity is used to cluster radiology reports and identify the repository’s underlying topics. We applied our approach on a repository of 1,899,482 radiology reports from three major healthcare organizations. Our method identified 19 major radiology report topics in the repository and clustered the reports accordingly to these topics. Our results are verified by a domain expert radiologist and successfully explain the repository’s primary topics and extract the corresponding reports. The results of our system provide a target-based corpus and framework for information extraction and retrieval systems for radiology reports.

Keywords

Topic modeling Radiology report narrative Clustering Text mining Natural language processing 

Notes

Acknowledgments

The authors would like to thank Chuck Kahn, Kevin McEnery, and Brad Erickson for their work on compiling RadCore database and Daniel Rubin for his contribution to RadCore and providing access to this database.

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Copyright information

© Society for Imaging Informatics in Medicine 2015

Authors and Affiliations

  1. 1.Department of RadiologyStanford UniversityStanfordUSA

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