Text Mining of Medical Documents in Spanish: Semantic Annotation and Detection of Recommendations

Carlos Tellería, Sergio Ilarri, Carlos Sánchez


In medical practice, identifying relevant facts and therapeutic recommendations from health-related documents is a key issue to ensure an efficient and effective service to patients. However, the automatic analysis of text documents to extract relevant data is a challenging task. This is the case particularly when we deal with documents written in languages other than English, for which the availability of lexical resources and tools is much more limited and less experiences have been reported. In this paper, we present our experience dealing with texts written in Spanish in a medical context. By applying text mining techniques and exploiting semantic resources, we present an approach to automatically label documents using appropriate medical terms. Besides, we also describe a technique that attempts to detect practice recommendations for doctors automatically in clinical guides. An experimental evaluation shows the benefits of applying text mining techniques as a support system for doctors as well as its feasibility. The scarcity of experimental evaluations with medical documents in Spanish motivated our work.


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