Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.19371 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929439534743552 |
|---|---|
| author | Xue, Yuan Zhou, Denny Du, Nan Dai, Andrew M. Xu, Zhen Zhang, Kun Cui, Claire |
| author_facet | Xue, Yuan Zhou, Denny Du, Nan Dai, Andrew M. Xu, Zhen Zhang, Kun Cui, Claire |
| contents | Capturing the inter-dependencies among multiple types of clinically-critical events is critical not only to accurate future event prediction, but also to better treatment planning. In this work, we propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events (e.g., kidney failure, mortality) by explicitly modeling the temporal dynamics of patients' latent states. Based on these learned patient states, we further develop a new general discrete-time formulation of the hazard rate function to estimate the survival distribution of patients with significantly improved accuracy. Extensive evaluations over real EMR data show that our proposed model compares favorably to various state-of-the-art baselines. Furthermore, our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19371 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep State-Space Generative Model For Correlated Time-to-Event Predictions Xue, Yuan Zhou, Denny Du, Nan Dai, Andrew M. Xu, Zhen Zhang, Kun Cui, Claire Machine Learning Capturing the inter-dependencies among multiple types of clinically-critical events is critical not only to accurate future event prediction, but also to better treatment planning. In this work, we propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events (e.g., kidney failure, mortality) by explicitly modeling the temporal dynamics of patients' latent states. Based on these learned patient states, we further develop a new general discrete-time formulation of the hazard rate function to estimate the survival distribution of patients with significantly improved accuracy. Extensive evaluations over real EMR data show that our proposed model compares favorably to various state-of-the-art baselines. Furthermore, our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures. |
| title | Deep State-Space Generative Model For Correlated Time-to-Event Predictions |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.19371 |