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
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Summary difficulty | Written by | Summary |
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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