Predicting high-cost patients by Machine Learning: A case study in an Australian private hospital group

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Eigner I, Bodendorf F, Wickramasinghe N
Editor(s): Oliver Eulenstein, Hisham Al-Mubaid and Qin Ding
Publication year: 2019
Conference Proceedings Title: Proceedings of 11th International Conference on Bioinformatics and Computational Biology
Pages range: 94-103
Language: English


Abstract

Healthcare is considered a data-intensive industry, offering large data volumes that can, for example, be used as the basis for data-driven decisions in hospital resource planning. A significant aspect in that context is the prediction of cost-intensive patients. The presented paper introduces prediction models to identify patients at risk of causing extensive costs to the hospital. Based on a data set from a private Australian hospital group, four logistic regression models designed and evaluated to predict cost-intensive patients. Each model utilizes different feature sets including attributes gradually available throughout a patient episode. The results show that in particular variables reflecting hospital resources have a high influence on the probability to become a cost-intensive patient. The corresponding prediction model that incorporates attributes describing resource utilization achieves a sensitivity of 94.32% and thus enables an effective prediction of cost-intensive patients.


FAU Authors / FAU Editors

Bodendorf, Freimut Prof. Dr.
Lehrstuhl für Wirtschaftsinformatik, insbesondere im Dienstleistungsbereich
Eigner, Isabella
Lehrstuhl für Wirtschaftsinformatik, insbesondere im Dienstleistungsbereich


Research Fields

Digital Analytics
Lehrstuhl für Wirtschaftsinformatik, insbesondere im Dienstleistungsbereich


How to cite

APA:
Eigner, I., Bodendorf, F., & Wickramasinghe, N. (2019). Predicting high-cost patients by Machine Learning: A case study in an Australian private hospital group. In Oliver Eulenstein, Hisham Al-Mubaid and Qin Ding (Eds.), Proceedings of 11th International Conference on Bioinformatics and Computational Biology (pp. 94-103). Honolulu, Hawaii, US.

MLA:
Eigner, Isabella, Freimut Bodendorf, and Nilmini Wickramasinghe. "Predicting high-cost patients by Machine Learning: A case study in an Australian private hospital group." Proceedings of the International Conference on Bioinformatics and Computational Biology,, Honolulu, Hawaii Ed. Oliver Eulenstein, Hisham Al-Mubaid and Qin Ding, 2019. 94-103.

BibTeX: 

Last updated on 2019-19-03 at 10:38