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

Autor:	Xue, Yuan, Zhou, Denny, Du, Nan, Dai, Andrew M., Xu, Zhen, Zhang, Kun, Cui, Claire
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.19371 Zobrazit plný text záznamu View this record from Arxiv