• Corpus ID: 7539338

Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier

@inproceedings{Futoma2017LearningTD,
  title={Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier},
  author={Joseph D. Futoma and Sanjay Hariharan and Katherine A. Heller},
  booktitle={International Conference on Machine Learning},
  year={2017}
}
We present a scalable end-to-end classifier that uses streaming physiological and medication data to accurately predict the onset of sepsis, a life-threatening complication from infections that has high mortality and morbidity. [] Key Method The Gaussian process is directly connected to a black-box classifier that predicts whether a patient will become septic, chosen in our case to be a recurrent neural network to account for the extreme variability in the length of patient encounters.

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