Abstract
As the adoption of Electronic Medical Records (EMRs) rises in the healthcare institutions, these resources’ importance increases because of the clinical information they contain about patients. However, the unstructured information in the form of the narrative present in those records makes it hard to extract and structure useful clinical information. This limits the potential of the EMRs, because the clinical information these records contain, can be used to perform important operations inside healthcare institutions such as searching, summarization, decision support and statistical analysis, as well as be used to support management decisions or serve for research. These operations can only be done if the clinical information from the narratives is properly extracted and structured. Usually, this extraction is made manually by healthcare practitioners, what is not efficient and is error-prone. This research uses Natural Language Processing (NLP) and Information Extraction (IE) techniques in order to develop a pipeline system that can extract and structure clinical information directly from the clinical narratives present in Portuguese EMRs, in an automated way, in order to help EMRs to fulfil their potential.
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Lamy, M., Pereira, R., Ferreira, J.C., Vasconcelos, J.B., Melo, F., Velez, I. (2019). Extracting Clinical Information from Electronic Medical Records. In: Novais, P., et al. Ambient Intelligence – Software and Applications –, 9th International Symposium on Ambient Intelligence. ISAmI2018 2018. Advances in Intelligent Systems and Computing, vol 806. Springer, Cham. https://doi.org/10.1007/978-3-030-01746-0_13
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DOI: https://doi.org/10.1007/978-3-030-01746-0_13
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