# Disease-Atlas: Navigating Disease Trajectories with Deep Learning

arXiv: https://arxiv.org/abs/1803.10254 · HTML: https://mlsanity.com/paper/1803.10254/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2018.

## Built on by (most cited first, 8 of 8)

- [MOTOR: A Time-To-Event Foundation Model For Structured Medical Records](https://mlsanity.com/paper/2301.03150/) (2023)
- [Causal Machine Learning for Healthcare and Precision Medicine](https://mlsanity.com/paper/2205.11402/) (2022)
- [Clairvoyance: A Pipeline Toolkit for Medical Time Series](https://mlsanity.com/paper/2310.18688/) (2023)
- [Online Decision Mediation](https://mlsanity.com/paper/2310.18601/) (2023)
- [TransformerLSR: Attentive Joint Model of Longitudinal Data, Survival, and Recurrent Events with Concurrent Latent Structure](https://mlsanity.com/paper/2404.03804/) (2024)
- [CTP:A Causal Interpretable Model for Non-Communicable Disease Progression Prediction](https://mlsanity.com/paper/2308.09735/) (2023)
- [Interpretable machine learning for high-dimensional trajectories of aging health](https://mlsanity.com/paper/2105.03410/) (2021)
- [WTNN: Weibull-Tailored Neural Networks for survival analysis](https://mlsanity.com/paper/2512.09163/) (2025)

## Abstract (arXiv)

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.