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Journal articles
Automatic classification of radiological reports for clinical care
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.
https://hal.archives-ouvertes.fr/hal-01844840
Contributor : Anne-Lyse Minard <>
Submitted on : Thursday, July 19, 2018 - 5:38:02 PM
Last modification on : Tuesday, February 25, 2020 - 8:08:12 AM
Contributor : Anne-Lyse Minard <>
Submitted on : Thursday, July 19, 2018 - 5:38:02 PM
Last modification on : Tuesday, February 25, 2020 - 8:08:12 AM
