Eigner, IsabellaBodendorf, Freimut2021-06-212021-06-212018https://dl.gi.de/handle/20.500.12116/36615Readmission prediction in hospitals is a highly complex task involving multiple risk factors that can vary among different disease groups. We address this issue by implementing multiple cross-validated classification models within an intelligent CDSS to enhance patient discharge management. Depending on the diagnosis, the system selects and applies the appropriate model and visualises the prediction results. In addition, the cost and reimbursement development for each episode are determined. The architecture of the CDSS and the integration of the prediction models are presented in this paper.enDecision supportIDSSCDSSreadmissionsrisk predictionmachine learningText/Journal Article10.1515/itit-2018-00032196-7032