tdCoxSNN: Time-Dependent Cox Survival Neural Network for Continuous-time Dynamic Prediction

• URL: http://arxiv.org/abs/2307.05881v2
• Date: Tue, 12 Mar 2024 14:09:56 GMT
• Title: tdCoxSNN: Time-Dependent Cox Survival Neural Network for Continuous-time Dynamic Prediction
• Authors: Lang Zeng, Jipeng Zhang, Wei Chen, Ying Ding
• Abstract summary: We propose a time-dependent Cox survival neural network (tdCoxSNN) to predict its progression using longitudinal fundus images. We evaluate and compare our proposed method with joint modeling and landmarking approaches through extensive simulations.
• Score: 19.38247205641199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The aim of dynamic prediction is to provide individualized risk predictions over time, which are updated as new data become available. In pursuit of constructing a dynamic prediction model for a progressive eye disorder, age-related macular degeneration (AMD), we propose a time-dependent Cox survival neural network (tdCoxSNN) to predict its progression using longitudinal fundus images. tdCoxSNN builds upon the time-dependent Cox model by utilizing a neural network to capture the non-linear effect of time-dependent covariates on the survival outcome. Moreover, by concurrently integrating a convolutional neural network (CNN) with the survival network, tdCoxSNN can directly take longitudinal images as input. We evaluate and compare our proposed method with joint modeling and landmarking approaches through extensive simulations. We applied the proposed approach to two real datasets. One is a large AMD study, the Age-Related Eye Disease Study (AREDS), in which more than 50,000 fundus images were captured over a period of 12 years for more than 4,000 participants. Another is a public dataset of the primary biliary cirrhosis (PBC) disease, where multiple lab tests were longitudinally collected to predict the time-to-liver transplant. Our approach demonstrates commendable predictive performance in both simulation studies and the analysis of the two real datasets.

Related papers

• A Framework for Evaluating Predictive Models Using Synthetic Image Covariates and Longitudinal Data [0.0]
  We present a novel framework for synthesizing patient data with longitudinal observations. Our approach introduces controlled association in latent spaces generating each data modality. We demonstrate our framework using optical coherence tomography ( OCT) scans.
  arXiv  Detail & Related papers  (2024-10-21T16:43:16Z)
• Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models [69.06149482021071]
  We propose a novel EHR data generation model called EHRPD. It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation. We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives.
  arXiv  Detail & Related papers  (2024-06-20T02:20:23Z)
• Value Prediction for Spatiotemporal Gait Data Using Deep Learning [0.19972837513980318]
  We expand application of deep learning to value prediction of time-seriestemporal gait data. Our results show that short-distance prediction has an RMSE as low as 0.060675, and long-distance prediction RMSE as low as 0.106365. The proposed, customized models, used with value prediction open possibilities for additional applications, such as fall prediction, in-home progress monitoring, aiding of exoskeleton movement, and authentication.
  arXiv  Detail & Related papers  (2024-02-29T18:30:13Z)
• 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)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Two-step interpretable modeling of Intensive Care Acquired Infections [0.0]
  We present a novel methodology for integrating high resolution longitudinal data with the dynamic prediction capabilities of survival models. The aim is two-fold: to improve the predictive power while maintaining interpretability of the models.
  arXiv  Detail & Related papers  (2023-01-26T14:54:17Z)
• FastCPH: Efficient Survival Analysis for Neural Networks [57.03275837523063]
  We propose FastCPH, a new method that runs in linear time and supports both the standard Breslow and Efron methods for tied events. We also demonstrate the performance of FastCPH combined with LassoNet, a neural network that provides interpretability through feature sparsity.
  arXiv  Detail & Related papers  (2022-08-21T03:35:29Z)
• 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)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Simulating time to event prediction with spatiotemporal echocardiography deep learning [12.059859769175281]
  New methods for time-to-event prediction have been developed by extending the cox-proportional hazards model with neural networks. We generate simulated survival datasets based on the expert annotated ejection fraction readings. By training on just the simulated survival outcomes, we show thattemporal convolutional neural networks yield accurate survival estimates.
  arXiv  Detail & Related papers  (2021-03-03T18:28:33Z)
• Predicting Patient Outcomes with Graph Representation Learning [0.47248250311484113]
  We propose to exploit diagnoses as temporal information by connecting similar patients in a graph. We demonstrate that LSTM-GNNs outperform the LSTM-only baseline on length of stay prediction tasks on the eICU database.
  arXiv  Detail & Related papers  (2021-01-11T15:04:07Z)

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.