A hospital wide predictive model for unplanned readmission using hierarchical ICD data
- Author
- Mieke Deschepper (UGent) , Kristof Eeckloo (UGent) , Dirk Vogelaers (UGent) and Willem Waegeman (UGent)
- Organization
- Keywords
- Readmission, Machine learning, Boosting, Random Forests, ICD-10 diagnosis, Decision support, RISK, IMPACT
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8603666
- MLA
- Deschepper, Mieke, et al. “A Hospital Wide Predictive Model for Unplanned Readmission Using Hierarchical ICD Data.” COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol. 173, 2019, pp. 177–83, doi:10.1016/j.cmpb.2019.02.007.
- APA
- Deschepper, M., Eeckloo, K., Vogelaers, D., & Waegeman, W. (2019). A hospital wide predictive model for unplanned readmission using hierarchical ICD data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 173, 177–183. https://doi.org/10.1016/j.cmpb.2019.02.007
- Chicago author-date
- Deschepper, Mieke, Kristof Eeckloo, Dirk Vogelaers, and Willem Waegeman. 2019. “A Hospital Wide Predictive Model for Unplanned Readmission Using Hierarchical ICD Data.” COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 173: 177–83. https://doi.org/10.1016/j.cmpb.2019.02.007.
- Chicago author-date (all authors)
- Deschepper, Mieke, Kristof Eeckloo, Dirk Vogelaers, and Willem Waegeman. 2019. “A Hospital Wide Predictive Model for Unplanned Readmission Using Hierarchical ICD Data.” COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 173: 177–183. doi:10.1016/j.cmpb.2019.02.007.
- Vancouver
- 1.Deschepper M, Eeckloo K, Vogelaers D, Waegeman W. A hospital wide predictive model for unplanned readmission using hierarchical ICD data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE. 2019;173:177–83.
- IEEE
- [1]M. Deschepper, K. Eeckloo, D. Vogelaers, and W. Waegeman, “A hospital wide predictive model for unplanned readmission using hierarchical ICD data,” COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol. 173, pp. 177–183, 2019.
@article{8603666,
author = {{Deschepper, Mieke and Eeckloo, Kristof and Vogelaers, Dirk and Waegeman, Willem}},
issn = {{0169-2607}},
journal = {{COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE}},
keywords = {{Readmission,Machine learning,Boosting,Random Forests,ICD-10 diagnosis,Decision support,RISK,IMPACT}},
language = {{eng}},
pages = {{177--183}},
title = {{A hospital wide predictive model for unplanned readmission using hierarchical ICD data}},
url = {{http://dx.doi.org/10.1016/j.cmpb.2019.02.007}},
volume = {{173}},
year = {{2019}},
}
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