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

• URL: http://arxiv.org/abs/2407.19371v1
• Date: Sun, 28 Jul 2024 02:42:36 GMT
• Title: Deep State-Space Generative Model For Correlated Time-to-Event Predictions
• Authors: Yuan Xue, Denny Zhou, Nan Du, Andrew M. Dai, Zhen Xu, Kun Zhang, Claire Cui,
• Abstract summary: We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
• Score: 54.3637600983898
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Conditional Score-Based Diffusion Model for Cortical Thickness Trajectory Prediction [29.415616701032604]
  Alzheimer's Disease (AD) is a neurodegenerative condition characterized by diverse progression rates among individuals. We propose a conditional score-based diffusion model to generate CTh trajectories with the given baseline information. Our model has a near-zero bias with narrow confidential 95% interval compared to the ground-truth CTh in 6-36 months.
  arXiv  Detail & Related papers  (2024-03-11T17:26:18Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Deep Vein Thrombosis (DVT) prediction [68.8204255655161]
  We propose a generative time-to-event model, SurvLatent ODE, which parameterizes a latent representation under irregularly sampled data. Our model then utilizes the latent representation to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function. SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying DVT risk groups.
  arXiv  Detail & Related papers  (2022-04-20T17:28:08Z)
• Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories [55.786207368853084]
  We address the problem of modeling constrained hospital resources in the midst of the COVID-19 pandemic. For broad applicability, we focus on the common yet challenging scenario where patient-level data for a region of interest are not available. We propose an aggregate count explicit-duration hidden Markov model, nicknamed the ACED-HMM, with an interpretable, compact parameterization.
  arXiv  Detail & Related papers  (2021-04-28T15:32:42Z)
• WRSE -- a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU [0.251657752676152]
  Dynamic assessment of mortality risk in the intensive care unit (ICU) can be used to stratify patients, inform about treatment effectiveness or serve as part of an early-warning system. We show competitive results with state-of-the-art probabilistic models, while greatly reducing training time by factors of 2-9x.
  arXiv  Detail & Related papers  (2020-11-02T10:13:59Z)
• Variational Disentanglement for Rare Event Modeling [21.269897066024306]
  We propose a variational disentanglement approach to learn from rare events in heavily imbalanced classification problems. Specifically, we leverage the imposed extreme-distribution behavior on a latent space to extract information from low-prevalence events.
  arXiv  Detail & Related papers  (2020-09-17T21:35:36Z)
• A General Framework for Survival Analysis and Multi-State Modelling [70.31153478610229]
  We use neural ordinary differential equations as a flexible and general method for estimating multi-state survival models. We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting.
  arXiv  Detail & Related papers  (2020-06-08T19:24:54Z)
• Deep Recurrent Model for Individualized Prediction of Alzheimer's Disease Progression [4.034948808542701]
  Alzheimer's disease (AD) is one of the major causes of dementia and is characterized by slow progression over several years. We propose a novel computational framework that can predict the phenotypic measurements of MRI biomarkers and trajectories of clinical status.
  arXiv  Detail & Related papers  (2020-05-06T08:08:00Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.