An intelligent decision support system for readmission prediction in healthcare
| dc.contributor.author | Eigner, Isabella | |
| dc.contributor.author | Bodendorf, Freimut | |
| dc.date.accessioned | 2021-06-21T10:12:43Z | |
| dc.date.available | 2021-06-21T10:12:43Z | |
| dc.date.issued | 2018 | |
| dc.description.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. | en |
| dc.identifier.doi | 10.1515/itit-2018-0003 | |
| dc.identifier.pissn | 2196-7032 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/36615 | |
| dc.language.iso | en | |
| dc.publisher | De Gruyter | |
| dc.relation.ispartof | it - Information Technology: Vol. 60, No. 4 | |
| dc.subject | Decision support | |
| dc.subject | IDSS | |
| dc.subject | CDSS | |
| dc.subject | readmissions | |
| dc.subject | risk prediction | |
| dc.subject | machine learning | |
| dc.title | An intelligent decision support system for readmission prediction in healthcare | en |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 205 | |
| gi.citation.publisherPlace | Berlin | |
| gi.citation.startPage | 195 |