Automatic Classification of Radiological Reports for Clinical Care

  • Alfonso E. Gerevini
  • Alberto Lavelli
  • Alessandro Maffi
  • Roberto Maroldi
  • Anne-Lyse MinardEmail author
  • Ivan Serina
  • Guido Squassina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10259)


Radiological reporting generates a large amount of free-text clinical narrative, a potentially valuable source of information for improving clinical care and supporting research. The use of automatic techniques to analyze such reports is necessary to make their content effectively available to radiologists in an aggregated form. In this paper we focus on the classification of chest computed tomography reports according to a classification schema proposed by radiologists of the Italian hospital ASST Spedali Civili di Brescia. At the time of writing, 346 reports have been annotated by a radiologist. Each report is classified according to the schema developed by radiologists and textual evidences are marked in the report. The annotations are then used to train different machine learning based classifiers. We present in this paper a method based on a cascade of classifiers which make use of a set of syntactic and semantic features. By testing the classifiers in cross-validation on manually annotated reports, we obtained a range of accuracy of 81–96%.


Support Vector Machine Classification Schema Machine Learning Technique Radiology Report Manual Annotation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research described in this paper has been partially supported by the Swiss National Science Foundation, grant number CR30I1_162758, and by the University of Brescia with the H&W SmartService project.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alfonso E. Gerevini
    • 1
  • Alberto Lavelli
    • 2
  • Alessandro Maffi
    • 1
  • Roberto Maroldi
    • 1
    • 3
  • Anne-Lyse Minard
    • 1
    • 2
    Email author
  • Ivan Serina
    • 1
  • Guido Squassina
    • 3
  1. 1.Università degli Studi di BresciaBresciaItaly
  2. 2.Fondazione Bruno KesslerTrentoItaly
  3. 3.Spedali Civili di BresciaBresciaItaly

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