Corpus ID: 237532503

Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series

@article{Rahman2021InterpretableAR,
  title={Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series},
  author={Asif Rahman and Yale Chang and Jonathan Rubin},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.07602}
}
Time series models with recurrent neural networks (RNNs) can have high accuracy but are unfortunately difficult to interpret as a result of feature-interactions, temporal-interactions, and nonlinear transformations. Interpretability is important in domains like healthcare where constructing models that provide insight into the relationships they have learned are required to validate and trust model predictions. We want accurate time series models that are interpretable where users can… 

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