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

Eigner I, Bodendorf F, Wickramasinghe N (2019)


Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2019

Pages Range: 94-103

Conference Proceedings Title: Proceedings of 11th International Conference on Bioinformatics and Computational Biology

Event location: Honolulu, Hawaii US

URI: https://easychair.org/publications/paper/pZqS

DOI: 10.29007/jw6h

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.

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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.

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