Abstract
We present in this article an innovative architecture of information extraction system applied to the medical domain. The content of documents (free texts) can be described or reduced to some relevant information. Our aim is to set a process in order to exploit efficiently the content of the documents. We will explain that the medical information extraction task can be analysed into three steps: Extraction “identify and extract a set of events and entities like date, names, medical terms”, Generation “create from these events and entities the relevant information”, Knowledge acquisition “validate and correct the extraction and generation results”. These analysis require to make various approaches in linguistic, statistic and artificial intelligent cooperate and use together specialised terminology as medical nomenclatures ICD-10 and CCMA and linguistic resources.
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Bekhouche, D., Pollet, Y., Grilheres, B., Denis, X. (2004). Architecture of a Medical Information Extraction System. In: Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2004. Lecture Notes in Computer Science, vol 3136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27779-8_35
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DOI: https://doi.org/10.1007/978-3-540-27779-8_35
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