Disease-Atlas: Navigating Disease Trajectories using Deep Learning

Bryan Lim, Mihaela van der Schaar
Proceedings of the 3rd Machine Learning for Healthcare Conference, PMLR 85:137-160, 2018.

Abstract

Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. While there are many advantages to joint modeling, the standard forms suffer from limitations that arise from a fixed model specification and computational difficulties when applied to high-dimensional datasets. In this paper, we propose a deep learning approach to address these limitations, enhancing existing methods with the inherent flexibility and scalability of deep neural networks while retaining the benefits of joint modeling. Using longitudinal data from the UK Cystic Fibrosis Trust, we demonstrate improvements in performance and scalability, as well as robustness in the presence of irregularly sampled data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v85-lim18a, title = {Disease-Atlas: Navigating Disease Trajectories using Deep Learning}, author = {Lim, Bryan and van der Schaar, Mihaela}, booktitle = {Proceedings of the 3rd Machine Learning for Healthcare Conference}, pages = {137--160}, year = {2018}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {85}, series = {Proceedings of Machine Learning Research}, month = {17--18 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v85/lim18a/lim18a.pdf}, url = {https://proceedings.mlr.press/v85/lim18a.html}, abstract = {Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. While there are many advantages to joint modeling, the standard forms suffer from limitations that arise from a fixed model specification and computational difficulties when applied to high-dimensional datasets. In this paper, we propose a deep learning approach to address these limitations, enhancing existing methods with the inherent flexibility and scalability of deep neural networks while retaining the benefits of joint modeling. Using longitudinal data from the UK Cystic Fibrosis Trust, we demonstrate improvements in performance and scalability, as well as robustness in the presence of irregularly sampled data.} }
Endnote
%0 Conference Paper %T Disease-Atlas: Navigating Disease Trajectories using Deep Learning %A Bryan Lim %A Mihaela van der Schaar %B Proceedings of the 3rd Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2018 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v85-lim18a %I PMLR %P 137--160 %U https://proceedings.mlr.press/v85/lim18a.html %V 85 %X Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. While there are many advantages to joint modeling, the standard forms suffer from limitations that arise from a fixed model specification and computational difficulties when applied to high-dimensional datasets. In this paper, we propose a deep learning approach to address these limitations, enhancing existing methods with the inherent flexibility and scalability of deep neural networks while retaining the benefits of joint modeling. Using longitudinal data from the UK Cystic Fibrosis Trust, we demonstrate improvements in performance and scalability, as well as robustness in the presence of irregularly sampled data.
APA
Lim, B. & van der Schaar, M.. (2018). Disease-Atlas: Navigating Disease Trajectories using Deep Learning. Proceedings of the 3rd Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 85:137-160 Available from https://proceedings.mlr.press/v85/lim18a.html.

Related Material