Loading Now

Summary of Deep State-space Generative Model For Correlated Time-to-event Predictions, by Yuan Xue and Denny Zhou and Nan Du and Andrew M. Dai and Zhen Xu and Kun Zhang and Claire Cui


Deep State-Space Generative Model For Correlated Time-to-Event Predictions

by Yuan Xue, Denny Zhou, Nan Du, Andrew M. Dai, Zhen Xu, Kun Zhang, Claire Cui

First submitted to arxiv on: 28 Jul 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: None

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This paper proposes a deep latent state-space generative model to capture the inter-dependencies among multiple clinically-critical events, such as kidney failure and mortality. The model explicitly models temporal dynamics in patients’ latent states, enabling better treatment planning and future event prediction. A new discrete-time formulation of the hazard rate function is developed for estimating survival distribution with improved accuracy. Evaluations on real EMR data show comparable performance to state-of-the-art baselines.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper helps doctors predict when patients might get sick again or die. They use a special computer model that looks at many different things happening in patients’ bodies, like kidney failure and organ problems. This model is better than others because it takes into account how these events happen over time. By using this model, doctors can make more informed decisions about treatment and help patients get the right care.

Keywords

* Artificial intelligence  * Generative model