• Computer Science
  • Published in ArXiv 2015

Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks

@article{Lipton2015PhenotypingOC,
  title={Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks},
  author={Zachary Chase Lipton and David C. Kale and Randall C. Wetzel},
  journal={ArXiv},
  year={2015},
  volume={abs/1510.07641}
}
We present a novel application of LSTM recurrent neural networks to multilabel classification of diagnoses given variable-length time series of clinical measurements. Our method outperforms a strong baseline on a variety of metrics. 
18
Twitter Mentions

Citations

Publications citing this paper.
SHOWING 1-10 OF 12 CITATIONS

Diagnosis Prediction via Recurrent Neural Networks

VIEW 4 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Recurrent Neural Networks for Autoregressive Moving Average Model Selection

  • 2019 IEEE International Conference on Big Knowledge (ICBK)
  • 2019
VIEW 1 EXCERPT
CITES METHODS

Auxiliary treatment of thyroid disease tensor combined with active learning method for multiple tasks

  • 2018 International Conference on Cloud Computing, Big Data and Blockchain (ICCBB)
  • 2018
VIEW 1 EXCERPT
CITES METHODS

References

Publications referenced by this paper.
SHOWING 1-10 OF 26 REFERENCES