Disease-Atlas: Navigating Disease Trajectories using Deep Learning

By Bryan Lim, Mihaela van der Schaar. 2018

39 citations recorded by the source corpus.

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 a real-world medical dataset, we demonstrate improvements in performance and scalability, as well as robustness in the presence of irregularly sampled data.

Subfields: Artificial Intelligence, Genetics, Management Science and Operations Research

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Citations in the corpus

Enhancing Time-Series Momentum Strategies Using Deep Neural Networks (2019, background): “such as demand forecasting [10], medicine [11] and finance [12].”
Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series (2018, background): “Recently, deep generative models have been proposed [10], sometimes even in combination with probabilistic modeling [23].”
SOM-VAE: Interpretable Discrete Representation Learning on Time Series (2019, background): “, 2017), sometimes even in combination with probabilistic modeling (Lim and Schaar, 2018).”