Semantic biomedical resource discovery: a Natural Language Processing framework

Abstract

BackgroundA 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.MethodsA 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/).ResultsFor 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.ConclusionsThere 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0200-4) ...

Model Annotation and Discovery with the Physiome Model Repository

“…exact matching. However, this could be handled with Natural Language Processing (NLP) techniques described in (Sfakianaki et al, 2014(Sfakianaki et al, , 2015 and by making SPARQL queries to the EBI SPARQL endpoint. For example, for a text "flux of sodium", NLP will parse the text into pieces with tokenization, lemmatization, and part of speech techniques and then SPARQL queries to the EBI SPARQL endpoints will map these pieces into reference ontology terms.…”

Participatory Aspects of ICT Infrastructures for Cancer Management

“…The CTMS can synchronize with the HIS using a sync service. Data entering the p-medicine environment are pseudonymized/anonymized and semantically annotated (Sfakianaki et al 2015). Access to external biobanks can be established and freely available data from the web can be stored in the data warehouse with the aid of literature mining (Potamias et al 2005).…”

Semantic annotation in biomedicine: the current landscape

“…Publicly available biomedical data, tools, services, models and computational workflows continuously increase in number, size and complexity. While this ever-growing abundance of valuable resources opens up unprecedented opportunities for biomedical research, it is also making it ever more challenging for researchers to efficiently discover and use the resources required for accomplishing their tasks [ 6 ]. Hence, automation of the search and discovery processes has turned into a necessity.…”

“…Based on such classification, clinical documents can be searched more effectively [ 7 ]. Furthermore, semantic concepts extracted from biomedical literature can also be used for semantic indexing and retrieval of biomedical publications [ 9 ] or biomedical tools and services [ 6 ]. In particular, biomedical Information Retrieval systems use semantic annotators to expand the users’ queries with concepts and terms from vocabularies/ontologies (mapping the query text to the appropriate ontology concepts, and then expanding the query with the terms associated with the mapped concepts), as well as to classify the retrieved documents based on their content or the occurrence of specific topics in the documents [ 1 ].…”

“…Biomedical texts have several characteristics that make them particularly challenging not only for semantic annotation, but for any NLP task [ 6 ]. Some of these characteristics include: Clinical text produced by practitioners often do not fully adhere to correct grammar, syntactic or spelling rules , as the following triage note illustrates: “SORE THROAT pt c/o sore throat x 1 week N pt states took antibiotic x 5 days after initiation of sore throat and sx resolved and now back after completed antibiotics N pt tolerating po fluids yet c/o pain on swallowing”; Biomedical terms are often polysemous and thus prone to ambiguity ; for example, an analysis of over 409 K Medline abstracts revealed that 11.7% of the phrases were ambiguous relative to the UMLS Metathesaurus [ 15 ].…”