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Methods and Applications of Informatics and Information Technology

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Volume 60, Issue 4

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An intelligent decision support system for readmission prediction in healthcare

Isabella Eigner
  • Corresponding author
  • Friedrich-Alexander Universität Erlangen-Nürnberg, Institute of Information Systems, D-90403 Nürnberg, Germany
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Freimut Bodendorf
Published Online: 2018-07-28 | DOI: https://doi.org/10.1515/itit-2018-0003

Abstract

Readmission 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.

Keywords: Decision support; IDSS; CDSS; readmissions; risk prediction; machine learning

ACM CCS: Information systemsInformation systems applicationsDecision support systemsData analytics

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About the article

Isabella Eigner

Isabella Eigner is a research assistant and PhD student at the Chair of Information Systems (Services – Processes – Intelligence) at the FAU Erlangen-Nuremberg in Nuremberg, Germany. Her research focuses on machine learning and risk prediction in healthcare.

Freimut Bodendorf

Freimut Bodendorf is a Professor of Information Systems and Head of the Institute of Information Systems at the FAU Erlangen-Nuremberg in Nuremberg, Germany.


Received: 2017-12-31

Revised: 2018-05-23

Accepted: 2018-05-24

Published Online: 2018-07-28

Published in Print: 2018-08-28


Citation Information: it - Information Technology, Volume 60, Issue 4, Pages 195–205, ISSN (Online) 2196-7032, ISSN (Print) 1611-2776, DOI: https://doi.org/10.1515/itit-2018-0003.

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