An Upper-Level Ontology for the Biomedical Domain

At the US National Library of Medicine we have developed the Unified Medical Language System (UMLS), whose goal it is to provide integrated access to a large number of biomedical resources by unifying the vocabularies that are used to access those resources. The UMLS currently interrelates some 60 controlled vocabularies in the biomedical domain. The UMLS coverage is quite extensive, including not only many concepts in clinical medicine, but also a large number of concepts applicable to the broad domain of the life sciences. In order to provide an overarching conceptual framework for all UMLS concepts, we developed an upper-level ontology, called the UMLS semantic network. The semantic network, through its 134 semantic types, provides a consistent categorization of all concepts represented in the UMLS. The 54 links between the semantic types provide the structure for the network and represent important relationships in the biomedical domain. Because of the growing number of information resources that contain genetic information, the UMLS coverage in this area is being expanded. We recently integrated the taxonomy of organisms developed by the NLM's National Center for Biotechnology Information, and we are currently working together with the developers of the Gene Ontology to integrate this resource, as well. As additional, standard, ontologies become publicly available, we expect to integrate these into the UMLS construct

From Genotype to Phenotype: Linking Bioinformatics and Medical Informatics Ontologies

A small group of around 40 people came together at the Chancellors Conference Centre in Manchester for the Ontologies Workshop, chaired by Alan Rector and Robert Stevens. The workshop was, rather strangely, spread over 2 half days. In hindsight, this programme worked very well as it gave people the opportunity to chat over a drink on the Saturday evening and share ideas, before launching into the second half on the following day. The participants were from various walks of life, all with a common interest in finding out more about ontologies and promoting collaborations between the medical informatics and bioinformatics ontology communities

KneeTex: an ontology–driven system for information extraction from MRI reports

Background. In the realm of knee pathology, magnetic resonance imaging (MRI) has the advantage of visualising all structures within the knee joint, which makes it a valuable tool for increasing diagnostic accuracy and planning surgical treatments. Therefore, clinical narratives found in MRI reports convey valuable diagnostic information. A range of studies have proven the feasibility of natural language processing for information extraction from clinical narratives. However, no study focused specifically on MRI reports in relation to knee pathology, possibly due to the complexity of knee anatomy and a wide range of conditions that may be associated with different anatomical entities. In this paper we describe KneeTex, an information extraction system that operates in this domain. Methods. As an ontology–driven information extraction system, KneeTex makes active use of an ontology to strongly guide and constrain text analysis. We used automatic term recognition to facilitate the development of a domain–specific ontology with sufficient detail and coverage for text mining applications. In combination with the ontology, high regularity of the sublanguage used in knee MRI reports allowed us to model its processing by a set of sophisticated lexico–semantic rules with minimal syntactic analysis. The main processing steps involve named entity recognition combined with coordination, enumeration, ambiguity and co–reference resolution, followed by text segmentation. Ontology–based semantic typing is then used to drive the template filling process. Results. We adopted an existing ontology, TRAK (Taxonomy for RehAbilitation of Knee conditions), for use within KneeTex. The original TRAK ontology expanded from 1,292 concepts, 1,720 synonyms and 518 relationship instances to 1,621 concepts, 2,550 synonyms and 560 relationship instances. This provided KneeTex with a very fine–grained lexico–semantic knowledge base, which is highly attuned to the given sublanguage. Information extraction results were evaluated on a test set of 100 MRI reports. A gold standard consisted of 1,259 filled template records with the following slots: finding, finding qualifier, negation, certainty, anatomy and anatomy qualifier. KneeTex extracted information with precision of 98.00%, recall of 97.63% and F–measure of 97.81%, the values of which are in line with human–like performance. Conclusions. KneeTex is an open–source, stand–alone application for information extraction from narrative reports that describe an MRI scan of the knee. Given an MRI report as input, the system outputs the corresponding clinical findings in the form of JavaScript Object Notation objects. The extracted information is mapped onto TRAK, an ontology that formally models knowledge relevant for the rehabilitation of knee conditions. As a result, formally structured and coded information allows for complex searches to be conducted efficiently over the original MRI reports, thereby effectively supporting epidemiologic studies of knee conditions

Concept Based Intermedia Medical Indexing. Application on CLEF Medical Image with

Content Based Medical Image Retrieval (CBMIR) has reached a very challenging threshold, related to the gap between low-level medical image features and the semantic highly specialized medical information and knowledge; to the important context-dependence of the query and navigation; and the wide distribution of the medical data and knowledge. Answers to questions concerning semantic descriptors, medical image analysis and report fusion and indexing, context-sensitive navigation and querying are thus still missing today. The medical imaging community has become increasingly aware of the potential benefit of using the new technologies in medical image analysis and retrieval, relating to diagnosis and prognosis assistance, evidence-based medicine and medical case-based reasoning. Besides the growing amount of medical data produced everyday, medical image retrieval systems have good potential in clinical decision making process, where it can be beneficial to find other images of the same modality, of the same anatomic region, and of the same disease. Hence, CBMIR systems can assist doctors in diagnosis by retrieving images with know