Download PDFOpen PDF in browserPredicting high-cost patients by Machine Learning: A case study in an Australian private hospital group10 pages•Published: March 18, 2019AbstractHealthcare 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.Keyphrases: cost intensive, healthcare, machine learning, prediction, predictive analytics In: Oliver Eulenstein, Hisham Al-Mubaid and Qin Ding (editors). Proceedings of 11th International Conference on Bioinformatics and Computational Biology, vol 60, pages 94-103.
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