Survival Prediction Using Deep Learning

Aliasghar Tarkhan, Noah Simon, Thomas Bengtsson, Kien Nguyen, Jian Dai
Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, PMLR 146:207-214, 2021.

Abstract

In many biomedical applications, outcome is measured as a “time-to-event” (e.g., time-to-disease progression or death). Cox proportional hazards (CoxPH) model has been widely used to assess the association between baseline characteristics of a patient and this outcome. Meanwhile, in therapeutic areas such as Oncology, clinical imaging (e.g. computerized tomography (CT) scan) is widely used for detection, diagnosis of disease, monitoring of progression and treatment effect. We are interested in using such images with neural network to build predictive models with survival data. However, the standard methodologies cannot be applied to imaging data with time-to-event outcome due to challenges such as memory constraint. In this work, we develop a simple methodology to engage images with survival data. Our proposed methodology is a modified version of CoxPH model that is amenable to SGD and allows us to overcome the existing challenges. We present the neural network architecture for the survival prediction using images. Our architecture can leverage new advances in network topology.

Cite this Paper

BibTeX
@InProceedings{pmlr-v146-tarkhan21a, title = {Survival Prediction Using Deep Learning}, author = {Tarkhan, Aliasghar and Simon, Noah and Bengtsson, Thomas and Nguyen, Kien and Dai, Jian}, booktitle = {Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021}, pages = {207--214}, year = {2021}, editor = {Greiner, Russell and Kumar, Neeraj and Gerds, Thomas Alexander and van der Schaar, Mihaela}, volume = {146}, series = {Proceedings of Machine Learning Research}, month = {22--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v146/tarkhan21a/tarkhan21a.pdf}, url = {https://proceedings.mlr.press/v146/tarkhan21a.html}, abstract = {In many biomedical applications, outcome is measured as a “time-to-event” (e.g., time-to-disease progression or death). Cox proportional hazards (CoxPH) model has been widely used to assess the association between baseline characteristics of a patient and this outcome. Meanwhile, in therapeutic areas such as Oncology, clinical imaging (e.g. computerized tomography (CT) scan) is widely used for detection, diagnosis of disease, monitoring of progression and treatment effect. We are interested in using such images with neural network to build predictive models with survival data. However, the standard methodologies cannot be applied to imaging data with time-to-event outcome due to challenges such as memory constraint. In this work, we develop a simple methodology to engage images with survival data. Our proposed methodology is a modified version of CoxPH model that is amenable to SGD and allows us to overcome the existing challenges. We present the neural network architecture for the survival prediction using images. Our architecture can leverage new advances in network topology.} }
Endnote
%0 Conference Paper %T Survival Prediction Using Deep Learning %A Aliasghar Tarkhan %A Noah Simon %A Thomas Bengtsson %A Kien Nguyen %A Jian Dai %B Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021 %C Proceedings of Machine Learning Research %D 2021 %E Russell Greiner %E Neeraj Kumar %E Thomas Alexander Gerds %E Mihaela van der Schaar %F pmlr-v146-tarkhan21a %I PMLR %P 207--214 %U https://proceedings.mlr.press/v146/tarkhan21a.html %V 146 %X In many biomedical applications, outcome is measured as a “time-to-event” (e.g., time-to-disease progression or death). Cox proportional hazards (CoxPH) model has been widely used to assess the association between baseline characteristics of a patient and this outcome. Meanwhile, in therapeutic areas such as Oncology, clinical imaging (e.g. computerized tomography (CT) scan) is widely used for detection, diagnosis of disease, monitoring of progression and treatment effect. We are interested in using such images with neural network to build predictive models with survival data. However, the standard methodologies cannot be applied to imaging data with time-to-event outcome due to challenges such as memory constraint. In this work, we develop a simple methodology to engage images with survival data. Our proposed methodology is a modified version of CoxPH model that is amenable to SGD and allows us to overcome the existing challenges. We present the neural network architecture for the survival prediction using images. Our architecture can leverage new advances in network topology.
APA
Tarkhan, A., Simon, N., Bengtsson, T., Nguyen, K. & Dai, J.. (2021). Survival Prediction Using Deep Learning. Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, in Proceedings of Machine Learning Research 146:207-214 Available from https://proceedings.mlr.press/v146/tarkhan21a.html.

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