Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation

• URL: http://arxiv.org/abs/2410.01847v2
• Date: Fri, 4 Oct 2024 01:55:55 GMT
• Title: Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation
• Authors: Omkar Kulkarni, Rohitash Chandra,
• Abstract summary: We propose a Bayesian Context-Aware Time Series Imputation (Bayes-CATSI) framework. We consider the time series derived from electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), electrocardiology (EKG) Our results show that Bayes-CATSI not only provides uncertainty quantification but also achieves superior imputation performance compared to the CATSI model.
• Score: 0.196629787330046
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Medical time series datasets feature missing values that need data imputation methods, however, conventional machine learning models fall short due to a lack of uncertainty quantification in predictions. Among these models, the CATSI (Context-Aware Time Series Imputation) stands out for its effectiveness by incorporating a context vector into the imputation process, capturing the global dependencies of each patient. In this paper, we propose a Bayesian Context-Aware Time Series Imputation (Bayes-CATSI) framework which leverages uncertainty quantification offered by variational inference. We consider the time series derived from electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), electrocardiology (EKG). Variational Inference assumes the shape of the posterior distribution and through minimization of the Kullback-Leibler(KL) divergence it finds variational densities that are closest to the true posterior distribution. Thus , we integrate the variational Bayesian deep learning layers into the CATSI model. Our results show that Bayes-CATSI not only provides uncertainty quantification but also achieves superior imputation performance compared to the CATSI model. Specifically, an instance of Bayes-CATSI outperforms CATSI by 9.57 %. We provide an open-source code implementation for applying Bayes-CATSI to other medical data imputation problems.

Related papers

• BAMITA: Bayesian Multiple Imputation for Tensor Arrays [4.111762232988317]
  We propose a multiple imputation approach for tensors in a flexible Bayesian framework. Our model uses efficient and widely applicable conjugate priors for a CANDECOMP/PARAFAC (CP) factorization. It is shown to accurately capture uncertainty in the full microbiome profile at missing timepoints and used to infer trends in species diversity for the population.
  arXiv  Detail & Related papers  (2024-10-30T19:30:23Z)
• 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)
• What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization [111.55277952086155]
  We study In-Context Learning (ICL) by addressing several open questions. We show that, without updating the neural network parameters, ICL implicitly implements the Bayesian model averaging algorithm. We prove that the error of pretrained model is bounded by a sum of an approximation error and a generalization error.
  arXiv  Detail & Related papers  (2023-05-30T21:23:47Z)
• 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)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection [0.0]
  Uncertainty is important in critical applications such as anomaly or lesion detection and clinical diagnosis. In this work, we focus on using quantile regression to estimate aleatoric uncertainty and use it for estimating uncertainty in both supervised and unsupervised lesion detection problems. We show how quantile regression can be used to characterize expert disagreement in the location of lesion boundaries.
  arXiv  Detail & Related papers  (2021-09-20T08:50:21Z)
• CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [107.63407690972139]
  Conditional Score-based Diffusion models for Imputation (CSDI) is a novel time series imputation method that utilizes score-based diffusion models conditioned on observed data. CSDI improves by 40-70% over existing probabilistic imputation methods on popular performance metrics. In addition, C reduces the error by 5-20% compared to the state-of-the-art deterministic imputation methods.
  arXiv  Detail & Related papers  (2021-07-07T22:20:24Z)
• Imputation-Free Learning from Incomplete Observations [73.15386629370111]
  We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation. We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation. Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
  arXiv  Detail & Related papers  (2021-07-05T12:44:39Z)
• About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data Annotations [2.3204178451683264]
  Variance Aware Training (VAT) method exploits this property by introducing the variance error into the model loss function. We validate VAT on three medical imaging datasets from diverse domains and various learning objectives.
  arXiv  Detail & Related papers  (2021-05-28T21:34:04Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)

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.