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Auflistung nach Autor:in "Tresp, Volker"

1 - 4 von 4
  • Zeitschriftenartikel
    Exploiting Latent Embeddings of Nominal Clinical Data for Predicting Hospital Readmission
    (KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Krompaß, Denis; Esteban, Cristóbal; Tresp, Volker; Sedlmayr, Martin; Ganslandt, Thomas
    Hospital readmissions of patients put a high burden not only on the health care system, but also on the patients since complications after discharge generally lead to additional burdens. Estimating the risk of readmission after discharge from inpatient care has been the subject of several publications in recent years. In those publications the authors mostly tried to directly infer the readmission risk (within a certain time frame) from the clinical data recorded in the medical routine such as primary diagnosis, co-morbidities, length of stay, or questionnaires. Instead of using these data directly as inputs for a prediction model, we are exploiting latent embeddings for the nominal parts of the data (e.g., diagnosis and procedure codes). These latent embeddings have been used with great success in the natural language processing domain and can be constructed in a preprocessing step. We show in our experiments, that a prediction model that exploits these latent embeddings can lead to improved readmission predictive models.
  • Textdokument
    Künstliche Intelligenz – Die dritte Welle
    (INFORMATIK 2020, 2021) Schmid, Ute; Tresp, Volker; Bethge, Matthias; Kersting, Kristian; Stiefelhagen, Rainer
    Aktuelle Forschungsarbeiten aus dem Bereich Künstlichen Intelligenz werden vorgestellt. Dabei werden drei Perspektiven auf das Gebiet Maschinelles Lernen präsentiert, die über rein datenintensive Blackbox-Verfahren hinausgehen: Es werden Methoden vorgestellt, mit denen Erklärungen für die Entscheidungen von KI-Systemen generiert werden, aktuelle neurowissenschaftlich Ansätze zum maschinellen Sehen gezeigt und eine Möglichkeit Vorwissen in den Prozess des machinellen Lernens einzubringen aufgezeigt.
  • Zeitschriftenartikel
    Relational and Fine-Grained Argument Mining
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Trautmann, Dietrich; Fromm, Michael; Tresp, Volker; Seidl, Thomas; Schütze, Hinrich
    In our project ReMLAV , funded within the DFG Priority Program RATIO ( http://www.spp-ratio.de/ ), we focus on relational and fine-grained argument mining. In this article, we first introduce the problems we address and then summarize related work. The main part of the article describes our research on argument mining, both coarse-grained and fine-grained methods, and on same-side stance classification, a relational approach to the problem of stance classification. We conclude with an outlook.
  • 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.