@article{AurastGradlPernesetal.2016,
  author    = {Aurast, Anna and Gradl, Tobias and Pernes, Stefan and Pielstr{\"o}m, Steffen},
  title     = {Big Data und Smart Data in den Geisteswissenschaften},
  series = {Bibliothek Forschung und Praxis},
  volume    = {40},
  journal   = {Bibliothek Forschung und Praxis},
  number    = {2},
  issn      = {1865-7648},
  doi       = {10.1515/bfp-2016-0033},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-195237},
  pages     = {200-206},
  year      = {2016},
  abstract  = {Kein Abstract verf{\"u}gbar.},
  language  = {de}
}
@article{LodaKrebsDanhofetal.2019,
  author    = {Loda, Sophia and Krebs, Jonathan and Danhof, Sophia and Schreder, Martin and Solimando, Antonio G. and Strifler, Susanne and Rasche, Leo and Kort{\"u}m, Martin and Kerscher, Alexander and Knop, Stefan and Puppe, Frank and Einsele, Hermann and Bittrich, Max},
  title     = {Exploration of artificial intelligence use with ARIES in multiple myeloma research},
  series = {Journal of Clinical Medicine},
  volume    = {8},
  journal   = {Journal of Clinical Medicine},
  number    = {7},
  issn      = {2077-0383},
  doi       = {10.3390/jcm8070999},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197231},
  pages     = {999},
  year      = {2019},
  abstract  = {Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented "A Rule-based Information Extraction System" (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.},
  language  = {en}
}
@article{AhmedZeeshanDandekar2016,
  author    = {Ahmed, Zeeshan and Zeeshan, Saman and Dandekar, Thomas},
  title     = {Mining biomedical images towards valuable information retrieval in biomedical and life sciences},
  series = {Database - The Journal of Biological Databases and Curation},
  volume    = {2016},
  journal   = {Database - The Journal of Biological Databases and Curation},
  doi       = {10.1093/database/baw118},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-162697},
  pages     = {baw118},
  year      = {2016},
  abstract  = {Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries.},
  language  = {en}
}