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A hospital wide predictive model for unplanned readmission using hierarchical ICD data

Mieke Deschepper (UGent) , Kristof Eeckloo (UGent) , Dirk Vogelaers (UGent) and Willem Waegeman (UGent)
Author
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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:

MLA
Deschepper, Mieke et al. “A Hospital Wide Predictive Model for Unplanned Readmission Using Hierarchical ICD Data.” COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 173 (2019): 177–183. Print.
APA
Deschepper, Mieke, 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.
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: 177–183.
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.
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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