# An Integrated Approach to Biomedical Term Identification Systems
## 2 Background  
The vast volume of biomedical unstructured documents generated on a daily basis is creating interesting challenges for the effective and efficient use of the information and knowledge stored in such texts. Some initiatives have recently emerged and attracted the interest of the research community. For example, 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. Medical information extraction and retrieval have also been relevant topics in the TREC community [8; 9]. In addition, the automatic or semi-automatic annotation of biomedical texts has gathered considerable attention in the past decade and several tools and resources have been developed mainly for English [10]. However, for other languages such as Spanish there is a lack of tools with similar levels of maturity imposing an imbalance in the access to health-related information for these languages. Thus, it is even more necessary to study technologies focused on the treatment of documents in Spanish. Actually, some research has begun to be carried out in recent years. For example, [11] propose a Spanish version of MetaMap combining machine translation and the original MetaMap in order to annotate Spanish texts with UMLS concepts. The Freeling analyzer was extended to develop Freeling-Med integrating ontologies and dictionaries to identify medical entities such as SNOMED-CT [12]. Finally, [13] present a prototype for the biomedical term normalization of Spanish electronic health records using UMLS. The approach applies information retrieval technologies to index the Metathesaurus, generating mapping candidates from input text. In addition, some recent medical NLP tasks have been proposed to develop Spanish NER in clinical records (MEDDOCAN [14] and PharmaCoNER [15]). Regarding medical ontologies like MeSH or the wider UMLS, we can consider as profitable and enriched source of semantic content, not only for finding papers in specialized literature [16], but also for clinical record retrieval [17; 18]. As can be checked from these references, the most common method for integrating medical ontologies in the retrieval process is by _query expansion_[19]. It involves recognizing entities in the query keywords and adding new concepts according to proximity in meaning or other taxonomic relations like meronymy or holomyny, for example. Most systems exhibit some sort of \"black-box\" approach in the retrieval process, i.e., the user enters a query or a sample document and the system retrieves all images, clinical cases, papers or whatever is indexed, trying to maximize the relevance of those retrieved items to the user's needs. Most evaluation forums currently propose this scenario, whereas user presence is somewhat neglected. In contrast to this understanding of the retrieval process, interactive approaches consider the intervention of the user to refine or fine tune the behavior of the system in order to obtain a better definition of user's needs, thus, improving the relevance of retrieved items. The system proposed by [PERSON] and his colleagues [20] enables the user to add new keywords taken from the MeSH thesaurus before performing query expansion, although in this study keywords are proposed automatically. This idea of allowing the user to refine the query is not new, as _interactive query expansion_ was proposed more than thirty years ago [21]. Although the evaluation of interactive systems is a drawback in the organization of evaluation tasks, as interactive systems evaluation is not an easy task some work has been done in this direction [22]. Nowadays, the interaction of users with medical retrieval systems has been found beneficial, improving user experience and the quality of retrieved items, as is the case for image retrieval [23] and diagnostic retrieval [24].