Abstract
In this paper a novel architecture to build biomedical term identification systems is presented. The architecture combines several sources of information and knowledge bases to provide practical and exploration-enabled biomedical term identification systems. We have implemented a system to evidence the convenience of the different modules considered in the architecture. Our system includes medical term identification, retrieval of specialized literature and semantic concept browsing from medical ontologies. By applying several Natural Language Processing (NLP) technologies, we have developed a prototype that offers an easy interface for helping to understand biomedical specialized terminology present in Spanish medical texts. The result is a system that performs term identification of medical concepts over any textual document written in Spanish. It is possible to perform a sub-concept selection using the previously identified terms to accomplish a fine-tune retrieval process over resources like SciELO, Google Scholar and MedLine. Moreover, the system generates a conceptual graph which semantically relates all the terms found in the text. In order to evaluate our proposal on medical term identification, we present the results obtained by our system using the MANTRA corpus and compare its performance with the Freeling-Med tool.
Highlights
In the biomedical domain we can find an impressive number of information sources
We have checked that only in the the first 200 documents, BSB annotate 346 concepts that are not taken into account in MANTRA Medline
Both systems behave in a similar manner, but thanks to the good recall reported by BSB, the F1 score increases considerably for the BSB system
Summary
In the biomedical domain we can find an impressive number of information sources. This information was difficult to access for different reasons, such as that the documents were not publicly available or the content was so complex that only medical specialists could understand the terminology used in the documents. In this paper we describe an approach to interactive information retrieval This approach combines and integrates different semantic resources such as: UMLS (Unified Medical Language System), Google. The CLEF eHealth Evaluation Lab is focused on combining NLP and information retrieval for clinical care [7]. This CLEF challenge has been running since 2013 and continues to propose different datasets and tasks every year. Some research has begun to be carried out in recent years
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