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RNN-SURV: A Deep Recurrent Model for Survival Analysis

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Artificial Neural Networks and Machine Learning – ICANN 2018 (ICANN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11141))

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Abstract

Current medical practice is driven by clinical guidelines which are designed for the “average” patient. Deep learning is enabling medicine to become personalized to the patient at hand. In this paper we present a new recurrent neural network model for personalized survival analysis called rnn-surv. Our model is able to exploit censored data to compute both the risk score and the survival function of each patient. At each time step, the network takes as input the features characterizing the patient and the identifier of the time step, creates an embedding, and outputs the value of the survival function in that time step. Finally, the values of the survival function are linearly combined to compute the unique risk score. Thanks to the model structure and the training designed to exploit two loss functions, our model gets better concordance index (C-index) than the state of the art approaches.

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Notes

  1. 1.

    https://www.unos.org/data/.

  2. 2.

    https://vincentarelbundock.github.io/Rdatasets/datasets.html/.

  3. 3.

    Implementation by lifelines package.

  4. 4.

    https://github.com/CamDavidsonPilon/lifelines/.

  5. 5.

    https://cran.r-project.org/web/packages/randomForestSRC/.

  6. 6.

    https://github.com/jaredleekatzman/DeepSurv/.

  7. 7.

    https://github.com/yanlirock/MTLSA/.

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Author information

Authors and Affiliations

  1. DIBRIS, Università di Genova, Genova, Italy

    Eleonora Giunchiglia

  2. Department of Electrical and Computer Engineering, UCLA, Los Angeles, USA

    Anton Nemchenko & Mihaela van der Schaar

  3. Department of Engineering Science, University of Oxford, Oxford, UK

    Mihaela van der Schaar

  4. Alan Turing Institute, London, UK

    Mihaela van der Schaar

Authors
  1. Eleonora Giunchiglia
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  2. Anton Nemchenko
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  3. Mihaela van der Schaar
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Corresponding author

Correspondence to Eleonora Giunchiglia .

Editor information

Editors and Affiliations

  1. Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  2. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

  3. CITEC Bielefeld University, Bielefeld, Germany

    Barbara Hammer

  4. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  5. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

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Giunchiglia, E., Nemchenko, A., van der Schaar, M. (2018). RNN-SURV: A Deep Recurrent Model for Survival Analysis. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-01424-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01423-0

  • Online ISBN: 978-3-030-01424-7

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