Automatic classification of radiological reports for clinical care

Alfonso Gerevini 1 Alberto Lavelli 2 Alessandro Maffi 1 Roberto Maroldi 1, 3 Anne-Lyse Minard 4, 5, 2 Ivan Serina 1 Guido Squassina 3
1 Università degli Studi di Brescia [Brescia]
2 Human Language Technology [Trento]
FBK - Fondazione Bruno Kessler [Trento, Italy]
3 Azienda Socio Sanitaria Territoriale Spedali Civili di Brescia [Brescia]
4 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA_D6 - MEDIA ET INTERACTIONS
5 CNRS - Centre National de la Recherche Scientifique
Abstract : Radiological reporting generates a large amount of free-text clinical narratives, 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 for this task by radiologists of the Italian hospital ASST Spedali Civili di Brescia. The proposed system is built exploiting a training data set containing reports annotated by radiologists. 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. The resulting system is a novel hierarchical classification system for the given task, that we have experimentally evaluated.
Type de document :
Article dans une revue
Artificial Intelligence in Medicine, Elsevier, 2018, pp.1-10. 〈10.1016/j.artmed.2018.05.006〉
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https://hal.archives-ouvertes.fr/hal-01844840
Contributeur : Anne-Lyse Minard <>
Soumis le : jeudi 19 juillet 2018 - 17:38:02
Dernière modification le : jeudi 15 novembre 2018 - 11:59:01

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CENTRALESUPELEC | IRISA | INRIA | UNIV-RENNES | UR1-UFR-ISTIC

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Alfonso Gerevini, Alberto Lavelli, Alessandro Maffi, Roberto Maroldi, Anne-Lyse Minard, et al.. Automatic classification of radiological reports for clinical care. Artificial Intelligence in Medicine, Elsevier, 2018, pp.1-10. 〈10.1016/j.artmed.2018.05.006〉. 〈hal-01844840〉

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