Unsupervised entity and relation extraction from clinical records in Italian

Abstract

This paper proposes and discusses the use of text mining techniques for the extraction of information from clinical records written in Italian. However, as it is very difficult and expensive to obtain annotated material for languages different from English, we only consider unsupervised approaches, where no annotated training set is necessary. We therefore propose a complete system that is structured in two steps. In the first one domain entities are extracted from the clinical records by means of a metathesaurus and standard natural language processing tools. The second step attempts to discover relations between the entity pairs extracted from the whole set of clinical records. For this last step we investigate the performance of unsupervised methods such as clustering in the space of entity pairs, represented by an ad hoc feature vector. The resulting clusters are then automatically labelled by using the most significant features. The system has been tested on a fairly large data set of clinical records in Italian, investigating the variation in the performance adopting different similarity measures in the feature space. The results of our experiments show that the unsupervised approach proposed is promising and well suited for a semi-automatic labelling of the extracted relations.

Keywords: Entities relation discovery; Entity extraction; Medical information extraction; Relation clustering; Unsupervised learning.