Modeling Progression Free Survival in Breast Cancer with Tensorized Recurrent Neural Networks and Accelerated Failure Time Models

Yinchong Yang; Peter A. Fasching; Volker Tresp

Modeling Progression Free Survival in Breast Cancer with Tensorized Recurrent Neural Networks and Accelerated Failure Time Models

Yinchong Yang, Peter A. Fasching, Volker Tresp ;

Proceedings of the 2nd Machine Learning for Healthcare Conference, PMLR 68:164-176, 2017.

Abstract

In this work we attempt to predict the progression-free survival time of metastatic breast cancer patients by combining state-of-the-art deep learning approaches with traditional survival analysis models. In order to tackle the challenge of sequential clinical records being both high-dimensional and sparse, we propose to apply a tensorized recurrent neural network architecture to extract a latent representation from the entire patient history. We use this as the input to an Accelerated Failure Time model that predicts the survival time. Our experiments, conducted on a large real-world clinical dataset, demonstrate that the tensorized recurrent neural network largely reduces the number of weight parameters and the training time. It also achieves modest improvements in prediction, in comparison with state-of-the-art recurrent neural network models enhanced with event embeddings.

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@InProceedings{pmlr-v68-yang17a, title = {Modeling Progression Free Survival in Breast Cancer with Tensorized Recurrent Neural Networks and Accelerated Failure Time Models}, author = {Yinchong Yang and Peter A. Fasching and Volker Tresp}, booktitle = {Proceedings of the 2nd Machine Learning for Healthcare Conference}, pages = {164--176}, year = {2017}, editor = {Finale Doshi-Velez and Jim Fackler and David Kale and Rajesh Ranganath and Byron Wallace and Jenna Wiens}, volume = {68}, series = {Proceedings of Machine Learning Research}, address = {Boston, Massachusetts}, month = {18--19 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v68/yang17a/yang17a.pdf}, url = {http://proceedings.mlr.press/v68/yang17a.html}, abstract = {In this work we attempt to predict the progression-free survival time of metastatic breast cancer patients by combining state-of-the-art deep learning approaches with traditional survival analysis models. In order to tackle the challenge of sequential clinical records being both high-dimensional and sparse, we propose to apply a tensorized recurrent neural network architecture to extract a latent representation from the entire patient history. We use this as the input to an Accelerated Failure Time model that predicts the survival time. Our experiments, conducted on a large real-world clinical dataset, demonstrate that the tensorized recurrent neural network largely reduces the number of weight parameters and the training time. It also achieves modest improvements in prediction, in comparison with state-of-the-art recurrent neural network models enhanced with event embeddings.} }

%0 Conference Paper %T Modeling Progression Free Survival in Breast Cancer with Tensorized Recurrent Neural Networks and Accelerated Failure Time Models %A Yinchong Yang %A Peter A. Fasching %A Volker Tresp %B Proceedings of the 2nd Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2017 %E Finale Doshi-Velez %E Jim Fackler %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v68-yang17a %I PMLR %J Proceedings of Machine Learning Research %P 164--176 %U http://proceedings.mlr.press %V 68 %W PMLR %X In this work we attempt to predict the progression-free survival time of metastatic breast cancer patients by combining state-of-the-art deep learning approaches with traditional survival analysis models. In order to tackle the challenge of sequential clinical records being both high-dimensional and sparse, we propose to apply a tensorized recurrent neural network architecture to extract a latent representation from the entire patient history. We use this as the input to an Accelerated Failure Time model that predicts the survival time. Our experiments, conducted on a large real-world clinical dataset, demonstrate that the tensorized recurrent neural network largely reduces the number of weight parameters and the training time. It also achieves modest improvements in prediction, in comparison with state-of-the-art recurrent neural network models enhanced with event embeddings.

Yang, Y., Fasching, P.A. & Tresp, V.. (2017). Modeling Progression Free Survival in Breast Cancer with Tensorized Recurrent Neural Networks and Accelerated Failure Time Models. Proceedings of the 2nd Machine Learning for Healthcare Conference, in PMLR 68:164-176