Deep Learning-Based Discrete Calibrated Survival Prediction

• URL: http://arxiv.org/abs/2208.08182v1
• Date: Wed, 17 Aug 2022 09:40:07 GMT
• Title: Deep Learning-Based Discrete Calibrated Survival Prediction
• Authors: Patrick Fuhlert, Anne Ernst, Esther Dietrich, Fabian Westhaeusser, Karin Kloiber, Stefan Bonn
• Abstract summary: We present Discrete Calibrated Survival (DCS), a novel deep neural network for discriminated and calibrated survival prediction. The enhanced performance of DCS can be attributed to two novel features, the variable temporal output node spacing and the novel loss term. We believe DCS is an important step towards clinical application of deep-learning-based survival prediction with state-of-the-art discrimination and good calibration.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep neural networks for survival prediction outper-form classical approaches in discrimination, which is the ordering of patients according to their time-of-event. Conversely, classical approaches like the Cox Proportional Hazards model display much better calibration, the correct temporal prediction of events of the underlying distribution. Especially in the medical domain, where it is critical to predict the survival of a single patient, both discrimination and calibration are important performance metrics. Here we present Discrete Calibrated Survival (DCS), a novel deep neural network for discriminated and calibrated survival prediction that outperforms competing survival models in discrimination on three medical datasets, while achieving best calibration among all discrete time models. The enhanced performance of DCS can be attributed to two novel features, the variable temporal output node spacing and the novel loss term that optimizes the use of uncensored and censored patient data. We believe that DCS is an important step towards clinical application of deep-learning-based survival prediction with state-of-the-art discrimination and good calibration.

Related papers

• SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network [4.772480981435387]
  We propose SurvCORN, a novel method utilizing conditional ordinal ranking networks to predict survival curves directly. We also introduce SurvMAE, a metric designed to evaluate the accuracy of model predictions in estimating time-to-event outcomes.
  arXiv  Detail & Related papers  (2024-09-30T03:01:25Z)
• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  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.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
  Sepsis is the leading cause of in-hospital mortality in the USA. Existing predictive models are usually trained on high-quality data with few missing information. For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
  arXiv  Detail & Related papers  (2024-07-24T04:47:36Z)
• Towards Clinician-Preferred Segmentation: Leveraging Human-in-the-Loop for Test Time Adaptation in Medical Image Segmentation [10.65123164779962]
  Deep learning-based medical image segmentation models often face performance degradation when deployed across various medical centers. We propose a novel Human-in-the-loop TTA framework that capitalizes on the largely overlooked potential of clinician-corrected predictions. Our framework conceives a divergence loss, designed specifically to diminish the prediction divergence instigated by domain disparities.
  arXiv  Detail & Related papers  (2024-05-14T02:02:15Z)
• 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)
• HypUC: Hyperfine Uncertainty Calibration with Gradient-boosted Corrections for Reliable Regression on Imbalanced Electrocardiograms [3.482894964998886]
  We propose HypUC, a framework for imbalanced probabilistic regression in medical time series. HypUC is evaluated on a large, diverse, real-world dataset of ECGs collected from millions of patients.
  arXiv  Detail & Related papers  (2023-11-23T06:17:31Z)
• Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data [89.79617468457393]
  Training models with imbalance rate (class density discrepancy) may lead to suboptimal prediction. We propose a framework for training models for this imbalance issue. We demonstrate our model's improved performance in real-world medical datasets.
  arXiv  Detail & Related papers  (2022-07-23T00:39:53Z)
• Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data [57.19441629270029]
  In this paper, we take advantage of the inherent properties of neural networks to federate the process of training of survival analysis models. In the realistic setting of small medical datasets and only a few data centers, this noise makes it harder for the models to converge. We propose DPFed-post which adds a post-processing stage to the private federated learning scheme.
  arXiv  Detail & Related papers  (2022-02-08T10:03:24Z)
• Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty [58.730520380312676]
  We show that differentially private gradient descent (DP-SGD) can yield poorly calibrated, overconfident deep learning models. This represents a serious issue for safety-critical applications, e.g. in medical diagnosis.
  arXiv  Detail & Related papers  (2021-07-09T08:14:45Z)
• Dynamic prediction of time to event with survival curves [3.9169188005935927]
  We apply our recently developed counterfactual dynamic survival model (CDSM) to static and longitudinal observational data. We prove that the inflection point of its estimated individual survival curves provides reliable prediction of the patient failure time.
  arXiv  Detail & Related papers  (2021-01-26T12:17:27Z)
• STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization [76.57716281104938]
  We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
  arXiv  Detail & Related papers  (2020-12-08T21:21:47Z)

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.