Predicting 30-day all-cause readmissions from hospital inpatient discharge data

@article{Yang2016Predicting3A,
  title={Predicting 30-day all-cause readmissions from hospital inpatient discharge data},
  author={Chengliang Yang and Chris Delcher and Elizabeth A. Shenkman and Sanjay Ranka},
  journal={2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)},
  year={2016},
  pages={1-6}
}
  • Chengliang YangC. Delcher S. Ranka
  • Published 1 September 2016
  • Medicine
  • 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)
Inpatient hospital readmissions for potentially avoidable conditions are problematic and costly. In this paper, we build machine learning models using variables widely available in health claims data to predict patients' 30-day readmission risks at the time of discharge. These models show high predictive power on a U.S. nationwide readmission database. They are also capable of providing interpretable risk factors globally at the population level and locally associated with each single discharge… 

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