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Type: workingPaper × Publisher: Hannover : Technische Informationsbibliothek ×

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    Analysis of Knowledge Tracing performance on synthesised student data
    (Hannover : Technische Informationsbibliothek, 2024) Pagonis, Panagiotis; Hartung, Kai; Wu, Di; Georges, Munir; Gröttrup, Sören
    Knowledge Tracing (KT) aims to predict the future performance of students by tracking the development of their knowledge states. Despite all the recent progress made in this field, the application of KT models in education systems is still restricted from the data perspectives: 1) limited access to real life data due to data protection concerns, 2) lack of diversity in public datasets, 3) noises in benchmark datasets such as duplicate records. To resolve these problems, we simulated student data with three statistical strategies based on public datasets and tested their performance on two KT baselines. While we observe only minor performance improvement with additional synthetic data, our work shows that using only synthetic data for training can lead to similar performance as real data.
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    Lebensqualität bei Krebserkrankungen – Integration in die Versorgung (working paper von Versorgern und Betroffenen)
    (Hannover : Technische Informationsbibliothek, 2024) Beutter, C. N. L.; Block, N.; Berger, S.; Edo-Ferrando, P.; Heinz, B.; Läufer, K.; Lang, B.; Mächtlen, K.; Münkel, S.; Rannert, D.; Zwerenz-Kopp, F.; Fegeler, C.
    Im Rahmen einer Projektgruppe haben sich sowohl Versorger (ÄrztInnen, KrebsberatungsstellenmitarbeiterInnen oder PsychoonkologInnen) sowie Betroffene mit einer regulären Integration von Lebensqualitätsdaten in der Versorgung befasst. Dabei wurde ein digitales System konzipiert, dass eine alltagsnahe und longitudinale Erhebung ermöglicht. Im working paper wurde über alle Teilnehmenden hinweg eine Problemidentifikation der derzeitigen IST-Situation integriert, um anhand dieser Probleme und Hemmschwellen ein übergreifendes Lösungskonzept zu erarbeiten. Im Lösungsraum wurden sowohl spezifische Anforderungen seitens der Versorger als auch PatientInnen zusammengefasst und gegenübergestellt. Dabei wurde ebenfalls die Vernetzung der einzelnen Akteure untereinander beleuchtet sowie die Thematik der Datenspende angerissen.
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    Bridging the Gap Between (AI-) Services and Their Application in Research and Clinical Settings Through Interoperability: the OMI-Protocol
    (Hannover : Technische Informationsbibliothek, 2024-02) Sigle, Stefan; Werner, Patrick; Schweizer, Simon; Caldeira, Liliana; Hosch, René; Dyrba, Martin; Fegeler, Christian; Sigle, Stefan; Werner, Patrick; Schweizer, Simon; Caldeira, Liliana; Hosch, René; Dyrba, Martin; Fegeler, Christian; Grönke, Ana; Seletkov, Dmitrii; Kotter, Elmar; Nensa, Felix; Wehrle, Julius; Kaufmes, Kevin; Scherer, Lucas; Nolden, Marco; Boeker, Martin; Schmidt, Marvin; Pelka, Obioma; Braren, Rickmer; Stump, Shura-Roman; Graetz, Teresa; Pogarell, Tobias; Susetzky, Tobias; Wieland, Tobias; Parmar, Vicky; Wang, Yuanbin
    Artificial Intelligence (AI) in research and clinical contexts is transforming the areas of medical and life sciences permanently. Aspects like findability, accessibility, interoperability, and reusability are often neglected for AI-based inference services. The Open Medical Inference (OMI) protocol aims to support remote inference by addressing the aforementioned aspects. Key component of the proposed protocol is an interoperable registry for remote inference services, which addresses the issue of findability for algorithms. It is complemented by information on how to invoke services remotely. Together, these components lay the basis for the implementation of distributed inference services beyond organizational borders. The OMI protocol considers prior work for aspects like data representation and transmission standards wherever possible. Based on Business Process Modeling of prototypical use cases for the service registry and common inference processes, a generic information model for remote services was inferred. Based on this model, FHIR resources were identified to represent AI-based services. The OMI protocol is first introduced using AI-services in radiology but is designed to be generalizable to other application domains as well. It provides an accessible, open specification as blueprint for the introduction and implementation of remote inference services.