In this paper, we investigate how semantic relations between concepts extracted from medical documents can be employed to improve the retrieval of medical literature. Semantic relations explicitly represent relatedness between concepts and carry high informative power that can be leveraged to improve the effectiveness of retrieval functionalities of clinical decision support systems. We present preliminary results and show how relations are able to provide a sizable increase of the precision for several topics, albeit having no impact on others. We then discuss some future directions to minimize the impact of negative results while maximizing the impact of good results.

A Relation Extraction Approach for Clinical Decision Support

Maristella Agosti;Giorgio Maria Di Nunzio;Stefano Marchesin
;
Gianmaria Silvello
2018

Abstract

In this paper, we investigate how semantic relations between concepts extracted from medical documents can be employed to improve the retrieval of medical literature. Semantic relations explicitly represent relatedness between concepts and carry high informative power that can be leveraged to improve the effectiveness of retrieval functionalities of clinical decision support systems. We present preliminary results and show how relations are able to provide a sizable increase of the precision for several topics, albeit having no impact on others. We then discuss some future directions to minimize the impact of negative results while maximizing the impact of good results.
2018
Proceedings of the CIKM 2018 Workshops co-located with 27th ACM International Conference on Information and Knowledge Management (CIKM 2018), Torino, Italy, October 22, 2018
ACM 12th International Workshop on Data and Text Mining in Biomedical Informatics (DTMBio)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3334132
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