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Auflistung nach Autor:in "Kreiner, Karl"

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  • Zeitschriftenartikel
    Designing Virtual Coaching Solutions
    (Business & Information Systems Engineering: Vol. 66, No. 3, 2024) Schlieter, Hannes; Gand, Kai; Weimann, Thure Georg; Sandner, Emanuel; Kreiner, Karl; Thoma, Steffen; Liu, Jin; Caprino, Massimo; Corbo, Massimo; Seregni, Agnese; Tropea, Peppino; Pino, Rocio; Gómez Esteban, Juan Carlos; Gabilondo, Inigo; Lacraru, Andreea Elena; Busnatu, Stefan Sebastian
    Especially older persons are prone to disabilities and chronic diseases. These chronic conditions pose a worldwide challenge, leading to deteriorating health, economic strain, loss of life, and a decline in the quality of life (QoL). Therefore, healthcare institutions seek to enhance their strategies for disease prevention and management to uphold the well-being of the community. This leads to the need to regain independence and improve QoL to properly rehabilitate the patients. Virtual Coaches (VCs) in the form of Embodied Conversational Agents are seen as a relevant digital intervention to support the continuity of care. The paper at hand reports on a Design Science Research project about implementing a VC solution to support older patients' home rehabilitation. The study underpins four pivotal design principles: Adaptivity, Coaching Strategy, Multi-user Interface, and Sustainable Infrastructure. The final artifact was tested with 80 patients which were supported in continuing their inpatient rehabilitation at home by using a VC. The evaluation shows both positive results for usability and acceptance of the intervention for four different use cases and a positive impact on the QoL. Given the comprehensive clinical evaluation, the system represents a safe and appealing solution for ensuring the continuity of medical rehabilitation care and the access to personalized cognitive and motor function treatments.
  • Zeitschriftenartikel
    Predictive analytics for data driven decision support in health and care
    (it - Information Technology: Vol. 60, No. 4, 2018) Hayn, Dieter; Veeranki, Sai; Kropf, Martin; Eggerth, Alphons; Kreiner, Karl; Kramer, Diether; Schreier, Günter
    Due to an ever-increasing amount of data generated in healthcare each day, healthcare professionals are more and more challenged with information. Predictive models based on machine learning algorithms can help to quickly identify patterns in clinical data. Requirements for data driven decision support systems for health and care ( DS4H ) are similar in many ways to applications in other domains. However, there are also various challenges which are specific to health and care settings. The present paper describes a) healthcare specific requirements for DS4H and b) how they were addressed in our Predictive Analytics Toolset for Health and care ( PATH ). PATH supports the following process: objective definition, data cleaning and pre-processing, feature engineering, evaluation, result visualization, interpretation and validation and deployment. The current state of the toolset already allows the user to switch between the various involved levels, i. e. raw data (ECG), pre-processed data (averaged heartbeat), extracted features (QT time), built models (to classify the ECG into a certain rhythm abnormality class) and outcome evaluation (e. g. a false positive case) and to assess the relevance of a given feature in the currently evaluated model as a whole and for the individual decision. This allows us to gain insights as a basis for improvements in the various steps from raw data to decisions.