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Descripción
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| The generalized adoption of Electronic Medical Records (EMR) together with the need to give the patient the appropriate treatment at the appropriate moment at the appropriate cost is demanding solutions to analyze the information on the EMR automatically. However most of the information on the EMR is non-structured: texts and images. Extracting knowledge from this data requires methods for structuring this information. Despite the efforts made in Natural Language Processing (NLP) even in the biomedical domain and in image processing, medical big data has still to undertake several challenges. The ungrammatical structure of clinical notes, abbreviations used and evolving terms have to be tackled in any Name Entity Recognition process. Moreover abbreviations, acronyms and terms are very much dependant on the language and the specific service. On the other hand, in the area of medical images, one of the main challenges is the development of new algorithms and methodologies that can help the physician take full advantage of the information contained in all these images. However, the large number of imaging modalities used today for diagnosis hinders the availability of general procedures as machine learning is, once again, a good approach for addressing this challenge. In this chapter, which concentrates on the problem of name entity recognition, we review previous approaches and look at future works. We also review the machine leaning approaches for image segmentation and annotation. | |
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Internacional
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Si |
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DOI
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10.1007/978-3-319-50478-0 |
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Edición del Libro
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1 |
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Editorial del Libro
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Springer International Publishing AG |
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ISBN
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978-3-319-50477-3 |
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Serie
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Lecture Notes in Computer Science |
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Título del Libro
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Machine Learning for Health Informatics |
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Desde página
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221 |
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Hasta página
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242 |