Paper Info
Title
Covid-19 Detection In Radiological Text Reports Integrating Entity Recognition
Abstract
COVID-19 diagnosis is usually based on PCR test using radiological images, mainly chest Computed Tomography (CT) for the assessment of lung involvement by COVID-19. However, textual radiological reports also contain relevant information for determining the likelihood of presenting radiological signs of COVID-19 involving lungs. The development of COVID-19 automatic detection systems based on Natural Language Processing (NLP) techniques could provide a great help in supporting clinicians and detecting COVID-19 related disorders within radiological reports. In this paper we propose a text classification system based on the integration of different information sources. The system can be used to automatically predict whether or not a patient has radiological findings consistent with COVID-19 on the basis of radiological reports of chest CT. To carry out our experiments we use 295 radiological reports from chest CT studies provided by the "HT me ' dica" clinic. All of them are radiological requests with suspicions of chest involvement by COVID-19. In order to train our text classification system we apply Machine Learning approaches and Named Entity Recognition. The system takes two sources of information as input: the text of the radiological report and COVID-19 related disorders extracted from SNOMEDCT. The best system is trained using SVM and the baseline results achieve 85% accuracy predicting lung involvement by COVID-19, which already offers competitive values that are difficult to overcome. Moreover, we apply mutual information in order to integrate the best quality information extracted from SNOMED-CT. In this way, we achieve around 90% accuracy improving the baseline results by 5 points.
Year
DOI
Venue
2020
10.1016/j.compbiomed.2020.104066
COMPUTERS IN BIOLOGY AND MEDICINE
Keywords
DocType
Volume
COVID-19, Radiological report, Text classification, Natural language processing, Named entity recognition
Journal
127
ISSN
Citations 
PageRank 
0010-4825
2
0.39
References 
Authors
0
6
Name
Order
Citations
PageRank
Pilar López-Úbeda122.75
Manuel Carlos Díaz-Galiano220.39
Teodoro Martín-Noguerol320.73
Antonio Luna420.73
Luis Alfonso Ureña López525753.93
Maite Martín-Valdivia6256.80