Special Issue on the Curative Power of Medical Data

With the massive amounts of medical data made available online, language technologies have proven to be indispensable in processing biomedical and molecular biology literature, health data or patient records. With huge amount of reports, evaluating their impact has long ceased to be a trivial task. Linking the contents of these documents to each other, as well as to specialized ontologies, could enable access to and the discovery of structured clinical information and could foster a major leap in natural language processing and in health research. The aim of this Special Issue, “Curative Power of Medical Data” in Data, is to gather innovative approaches for the exploitation of biomedical data using semantic web technologies and linked data by developing a community involvement in biomedical research. This Special Issue contains four surveys, which include a wide range of topics, from the analysis of biomedical articles writing style, to automatically generating tests from medical references, constructing a Gold standard biomedical corpus or the visualization of biomedical data.

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[2]  Diana Trandabat,et al.  Towards Identifying Author Confidence in Biomedical Articles , 2019, Data.

[3]  Byunghan Lee,et al.  Deep learning in bioinformatics , 2016, Briefings Bioinform..

[4]  Verginica Barbu Mititelu,et al.  Towards the Construction of a Gold Standard Biomedical Corpus for the Romanian Language , 2018, Data.

[5]  Atul J Butte,et al.  Collaborative Biomedicine in the Age of Big Data: The Case of Cancer , 2014, Journal of medical Internet research.

[6]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[7]  Dan Wu,et al.  Evolutionary Path of Factors Influencing Life Satisfaction among Chinese Elderly: A Perspective of Data Visualization , 2018, Data.

[8]  Søren Brunak,et al.  A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts , 2018, PLoS Comput. Biol..

[9]  R Cornet,et al.  A Framework for Characterizing Terminological Systems , 2006, Methods of Information in Medicine.

[10]  Kenji Araki,et al.  The Development of MML (Medical Markup Language) Version 3.0 as a Medical Document Exchange Format for HL7 Messages , 2004, Journal of Medical Systems.

[11]  Marcus A Banks,et al.  The future of Biomedical Digital Libraries , 2006, Biomedical Digital Libraries.

[12]  N. D. de Keizer,et al.  Understanding Terminological Systems I: Terminology and Typology , 2000, Methods of Information in Medicine.

[13]  Zhiyong Lu,et al.  Crowdsourcing in biomedicine: challenges and opportunities , 2016, Briefings Bioinform..

[14]  Diana Trandabat,et al.  Medi-Test: Generating Tests from Medical Reference Texts , 2018, Data.