Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 15, 77

Semantic Biomedical Resource Discovery: A Natural Language Processing Framework

Affiliations

Semantic Biomedical Resource Discovery: A Natural Language Processing Framework

Pepi Sfakianaki et al. BMC Med Inform Decis Mak.

Abstract

Background: A plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate. In parallel, specialized repositories are been developed, indexing numerous clinical and biomedical tools. The main drawback of such repositories is the difficulty in locating appropriate resources for a clinical or biomedical decision task, especially for non-Information Technology expert users. In parallel, although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications has been slow and lags behind progress in the general NLP domain. The aim of the present study is to investigate the use of semantics for biomedical resources annotation with domain specific ontologies and exploit Natural Language Processing methods in empowering the non-Information Technology expert users to efficiently search for biomedical resources using natural language.

Methods: A Natural Language Processing engine which can "translate" free text into targeted queries, automatically transforming a clinical research question into a request description that contains only terms of ontologies, has been implemented. The implementation is based on information extraction techniques for text in natural language, guided by integrated ontologies. Furthermore, knowledge from robust text mining methods has been incorporated to map descriptions into suitable domain ontologies in order to ensure that the biomedical resources descriptions are domain oriented and enhance the accuracy of services discovery. The framework is freely available as a web application at ( http://calchas.ics.forth.gr/ ).

Results: For our experiments, a range of clinical questions were established based on descriptions of clinical trials from the ClinicalTrials.gov registry as well as recommendations from clinicians. Domain experts manually identified the available tools in a tools repository which are suitable for addressing the clinical questions at hand, either individually or as a set of tools forming a computational pipeline. The results were compared with those obtained from an automated discovery of candidate biomedical tools. For the evaluation of the results, precision and recall measurements were used. Our results indicate that the proposed framework has a high precision and low recall, implying that the system returns essentially more relevant results than irrelevant.

Conclusions: There are adequate biomedical ontologies already available, sufficiency of existing NLP tools and quality of biomedical annotation systems for the implementation of a biomedical resources discovery framework, based on the semantic annotation of resources and the use on NLP techniques. The results of the present study demonstrate the clinical utility of the application of the proposed framework which aims to bridge the gap between clinical question in natural language and efficient dynamic biomedical resources discovery.

Figures

Fig. 1
Fig. 1
The architecture of the framework. The architecture of the framework: 1) tools registration, 2) tools annotation, 3) user’s question in natural language and NLP processing, 4) form and send the query, and 5) retrieve results (related tools)
Fig. 2
Fig. 2
Annotation example from Concept Recognizer. The annotation from the Concept Recognizer of the given data sentence “John has lung cancer and has been treated with carboplatin which is known for toxicology adverse effects”

Similar articles

See all similar articles

Cited by 5 articles

References

    1. Zhu F, Patumcharoenpol P, Zhang C, Yang Y, Chan J, Meechai A, et al. Biomedical text mining and its applications in cancer research. J Biomed Inform. 2013;46:200–211. doi: 10.1016/j.jbi.2012.10.007. - DOI - PubMed
    1. Meystre S, Haug JP. Natural language processing to extract medical problems from electronic clinical documents: Performance evaluation. J Biomed Inform. 2006;39(6):589–599. doi: 10.1016/j.jbi.2005.11.004. - DOI - PubMed
    1. Wolstencroft K, Haines R, Fellows D, Williams A, Withers D, Owen S, et al. The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud. Nucleic Acids Res. 2013;41(W1):557–561. doi: 10.1093/nar/gkt328. - DOI - PMC - PubMed
    1. Goble CA, Bhagat J, Aleksejevs S, Cruickshank D, Michaelides D, Newman D, et al. myExperiment: a repository and social network for the sharing of bioinformatics workflows. Nucleic Acids Res. 2010;38(2):677–682. doi: 10.1093/nar/gkq429. - DOI - PMC - PubMed
    1. Bhagat J, Tanoh F, Nzuobontane E, Laurent T, Orlowski J, Roos M, et al. BioCatalogue: a universal catalogue of web services for the life sciences. Nucleic Acids Res. 2010;38(2):W689–W694. doi: 10.1093/nar/gkq394. - DOI - PMC - PubMed

Publication types

LinkOut - more resources

Feedback