Abstract
Road traffic accidents in Algeria are a major public health problem for children, since they are among the leading causes of death starting from the age of 1 year. Among these victims, traumatic brain injuries (TBIs) causing several sequelaes, which are the frequent causes of consultation in children. For a better support of these cases, we propose a Multi-Label Text Categorization (MLTC) framework to help health professionals for the best cerebral lesions’ identification found in TBIs that appear simultaneously. The aim through this article development is to evaluate a set of multi-label (ML) transformation approaches to detect cerebral lesions’ from medical reports collecting from the pediatric intensive care unit of Oran hospital -Algeria.
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Benfriha, H., Atmani, B., Khemliche, B., Aoul, N.T., Douah, A. (2019). A Multi-labels Text Categorization Framework for Cerebral Lesion’s Identification. In: Alfaries, A., Mengash, H., Yasar, A., Shakshuki, E. (eds) Advances in Data Science, Cyber Security and IT Applications. ICC 2019. Communications in Computer and Information Science, vol 1098. Springer, Cham. https://doi.org/10.1007/978-3-030-36368-0_10
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