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Europe PubMed Central
Article . 2015
Data sources: PubMed Central
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BMC Medical Informatics and Decision Making; OpenAPC Global Initiative
Other literature type . Article . Conference object . 2015 . Peer-reviewed
License: Springer TDM
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Semantic biomedical resource discovery: a Natural Language Processing framework

Authors: Sfakianaki, Pepi; Koumakis, Lefteris; Sfakianakis, Stelios; Iatraki, Galatia; Zacharioudakis, Giorgos; Graf, Norbert; Marias, Kostas; +1 Authors

Semantic biomedical resource discovery: a Natural Language Processing framework

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. Electronic supplementary material The online version of this article (doi:10.1186/s12911-015-0200-4) contains supplementary material, which is available to authorized users.

Subjects by Vocabulary

Microsoft Academic Graph classification: Natural language user interface Computer science computer.software_genre Semantics Health informatics Domain (software engineering) Resource (project management) business.industry Search engine indexing Data science Information extraction Artificial intelligence business computer Natural language processing Drawback

Keywords

Biomedical text annotation, Information extraction, Text mining, Semantic resource annotation, Natural language interface, Health Informatics, Search engine, Data Mining, Humans, Medical Informatics Applications, Natural language processing, Health Policy, Semantics, Computer Science Applications, Databases as Topic, Biomedical informatics, Resource discovery, Research Article

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    impulse
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    Top 10%
  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    28
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
28
Top 10%
Top 10%
Top 10%
Green
gold
Funded by
EC| P-MEDICINE
Project
P-MEDICINE
From data sharing and integration via VPH models to personalised medicine
  • Funder: European Commission (EC)
  • Project Code: 270089
  • Funding stream: FP7 | SP1 | ICT
,
EC| iManageCancer
Project
iManageCancer
iManageCancer - Empowering patients and strengthening self-management in cancer diseases
  • Funder: European Commission (EC)
  • Project Code: 643529
  • Funding stream: H2020 | RIA
Validated by funder | iis
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