Advances in Prediction of Readmission Rates Using Long Term Short Term Memory Networks on Healthcare Insurance Data

@article{Khalid2022AdvancesIP,
  title={Advances in Prediction of Readmission Rates Using Long Term Short Term Memory Networks on Healthcare Insurance Data},
  author={Shuja Khalid and Francisco Matos and Ayman N Abunimer and Joel Bartlett and Richard Duszak and Michal Horn{\'y} and Judy Wawira Gichoya and Imon Banerjee and Hari Trivedi},
  journal={ArXiv},
  year={2022},
  volume={abs/2207.00066}
}
Background 30-day hospital readmission is a long-standing medical problem that affects patients’ morbidity and mortality and costs billions of dollars annually. Recently, machine learning models have been created to predict risk of inpatient readmission for patients with specific diseases, however no model exists to predict this risk across all patients. Methods We developed a bi-directional Long Short Term Memory (LSTM) Network that is able to use readily available insurance data (inpatient… 

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