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) contains supplementary material, which is available to authorized users.
Highlights
A plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate
The first sentence represents the available knowledge of the clinician and mainly correlates to a tool’s inputs, i.e. “John has lung cancer and has been treated with carboplatin which is known for toxicology adverse effects.”, while the second sentence is the clinical hypothesis, the research question, and is mainly connected to a tool’s outputs, i.e. “I would like to find literature and reference related to such events for the specific drug”
This study focused on the development of a Semantic Biomedical Resource Discovery Framework by making use of natural language processing techniques
Summary
A plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate. A plethora of publicly available biomedical resources (data, tools, services, models and computational workflows) do currently exist and are constantly increasing at a fast rate This explosion of biomedical resources generates impediments for the biomedical researchers’ needs, in order to efficiently discover appropriate resources to accomplish their clinical tasks. It is extremely difficult to locate the necessary resources [1], especially for non-Information Technology (IT) expert users, because most of the available tools are commonly described via narrative web pages containing information about their operations in natural language or are annotated with relevant technical details which are not interpreted by lay users. Due to the fact that the range of accessible resources has been considerably expanded in recent years and a significant number of new such resource repositories have been developed, it has been more and more difficult for clinicians and researchers to locate the most appropriate resource for the realization of their tasks
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