Logo des Repositoriums

Auflistung nach Autor:in "Zillner, Sonja"

1 - 2 von 2
  • Zeitschriftenartikel
    Technology Roadmap Development for Big Data Healthcare Applications
    (KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Zillner, Sonja; Neururer, Sabrina
    Big data applications indicate a wide range of opportunities to improve the overall quality and efficiency of healthcare delivery. The highest impact of big data applications is expected when data from various healthcare areas, such as clinical, administrative, financial, or outcome data, can be integrated. However, as of today, the realization of big data healthcare applications aggregating various kinds of data sources is still lacking behind. In order to foster the implementation of comprehensive big data applications, a clear understanding of short-term and long-term goals of envisioned big data scenarios is needed to forecast which emerging big data technologies are needed at what point in time. The contribution of this paper is to introduce the development of a technology roadmap for big data technologies in the healthcare domain. Beside the description of user needs and the technologies needed in order to satisfy those needs, the technology roadmap provides a basis to forecast technology developments and, thus, guidance in planning and coordinating technology developments accordingly.
  • Zeitschriftenartikel
    The Clinical Data Intelligence Project
    (Informatik-Spektrum: Vol. 39, No. 4, 2016) Sonntag, Daniel; Tresp, Volker; Zillner, Sonja; Cavallaro, Alexander; Hammon, Matthias; Reis, André; Fasching, Peter A.; Sedlmayr, Martin; Ganslandt, Thomas; Prokosch, Hans-Ulrich; Budde, Klemens; Schmidt, Danilo; Hinrichs, Carl; Wittenberg, Thomas; Daumke, Philipp; Oppelt, Patricia G.
    This article is about a new project that combines clinical data intelligence and smart data. It provides an introduction to the “Klinische Datenintelligenz” (KDI) project which is founded by the Federal Ministry for Economic Affairs and Energy (BMWi); we transfer research and development results (R&D) of the analysis of data which are generated in the clinical routine in specific medical domain. We present the project structure and goals, how patient care should be improved, and the joint efforts of data and knowledge engineering, information extraction (from textual and other unstructured data), statistical machine learning, decision support, and their integration into special use cases moving towards individualised medicine. In particular, we describe some details of our medical use cases and cooperation with two major German university hospitals.