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%.

Automatic Classification of Radiological Reports for Clinical Care

Lavelli, Alberto;Minard, Anne-Lyse Myriam;
2017

Abstract

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%.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/310691
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