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2016 | OriginalPaper | Chapter

Semantic Cluster Labeling for Medical Relations

Authors : Anita Alicante, Anna Corazza, Francesco Isgrò, Stefano Silvestri

Published in: Innovation in Medicine and Healthcare 2016

Publisher: Springer International Publishing

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Abstract

In the context of the extraction of the semantic contents important for the effective exploitation of the documents which are now made available by medical information systems, we consider the processing of relations connecting named entities and propose an unsupervised approach to their recognition and labeling. The approach is applied to an Italian data set of medical reports, and interesting results are presented and discussed from a qualitative point of view.

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previous chapter An Ontology-Based Approach for Representing Medical Recommendations in mHealth Applications
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Footnotes
6
The software is freely available at https://​code.​google.​com/​p/​word2vec/​.
 
7
Nausea persists with vomiting, hiccups, and general malaise, fasting.
 
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Alicante, A., Corazza, A., Isgrò, F., Silvestri, S.: Unsupervised entity and relation extraction from clinical records in Italian. Comput. Biol. Med. (2016)
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Metadata
Title
Semantic Cluster Labeling for Medical Relations
Authors
Anita Alicante
Anna Corazza
Francesco Isgrò
Stefano Silvestri
Copyright Year
2016
Book
Innovation in Medicine and Healthcare 2016

Print ISBN: 978-3-319-39686-6

Electronic ISBN: 978-3-319-39687-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-39687-3_18

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