Medical data wrangling with sequential variational autoencoders

• URL: http://arxiv.org/abs/2103.07206v1
• Date: Fri, 12 Mar 2021 10:59:26 GMT
• Title: Medical data wrangling with sequential variational autoencoders
• Authors: Daniel Barrej\'on, Pablo M. Olmos, Antonio Art\'es-Rodr\'iguez
• Abstract summary: This paper proposes to model medical data records with heterogeneous data types and bursty missing data using sequential variational autoencoders (VAEs) We show that Shi-VAE achieves the best performance in terms of using both metrics, with lower computational complexity than the GP-VAE model.
• Score: 5.9207487081080705
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical data sets are usually corrupted by noise and missing data. These missing patterns are commonly assumed to be completely random, but in medical scenarios, the reality is that these patterns occur in bursts due to sensors that are off for some time or data collected in a misaligned uneven fashion, among other causes. This paper proposes to model medical data records with heterogeneous data types and bursty missing data using sequential variational autoencoders (VAEs). In particular, we propose a new methodology, the Shi-VAE, which extends the capabilities of VAEs to sequential streams of data with missing observations. We compare our model against state-of-the-art solutions in an intensive care unit database (ICU) and a dataset of passive human monitoring. Furthermore, we find that standard error metrics such as RMSE are not conclusive enough to assess temporal models and include in our analysis the cross-correlation between the ground truth and the imputed signal. We show that Shi-VAE achieves the best performance in terms of using both metrics, with lower computational complexity than the GP-VAE model, which is the state-of-the-art method for medical records.

Related papers

• 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)
• ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models [69.9178140563928]
  Colonoscopy analysis is essential for assisting clinical diagnosis and treatment. The scarcity of annotated data limits the effectiveness and generalization of existing methods. We propose an Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy images that benefit the downstream tasks.
  arXiv  Detail & Related papers  (2023-09-03T07:55:46Z)
• Learning from aggregated data with a maximum entropy model [73.63512438583375]
  We show how a new model, similar to a logistic regression, may be learned from aggregated data only by approximating the unobserved feature distribution with a maximum entropy hypothesis. We present empirical evidence on several public datasets that the model learned this way can achieve performances comparable to those of a logistic model trained with the full unaggregated data.
  arXiv  Detail & Related papers  (2022-10-05T09:17:27Z)
• MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data [59.26381272149325]
  We present an unsupervised random forest for representing data with disparate variable types. MURAL forests consist of a set of decision trees where node-splitting variables are chosen at random. We show that using our approach, we can visualize and classify data more accurately than competing approaches.
  arXiv  Detail & Related papers  (2021-11-19T22:02:21Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Sequential Diagnosis Prediction with Transformer and Ontological Representation [35.88195694025553]
  We propose an end-to-end robust transformer-based model called SETOR to handle irregular intervals between a patient's visits with admitted timestamps and length of stay in each visit. Experiments conducted on two real-world healthcare datasets show that, our sequential diagnoses prediction model SETOR achieves better predictive results than previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-09-07T13:09:55Z)
• Harmonization with Flow-based Causal Inference [12.739380441313022]
  This paper presents a normalizing-flow-based method to perform counterfactual inference upon a structural causal model (SCM) to harmonize medical data. We evaluate on multiple, large, real-world medical datasets to observe that this method leads to better cross-domain generalization compared to state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2021-06-12T19:57:35Z)
• Handling Non-ignorably Missing Features in Electronic Health Records Data Using Importance-Weighted Autoencoders [8.518166245293703]
  We propose a novel extension of VAEs called Importance-Weighted Autoencoders (IWAEs) to flexibly handle Missing Not At Random patterns in the Physionet data. Our proposed method models the missingness mechanism using an embedded neural network, eliminating the need to specify the exact form of the missingness mechanism a priori.
  arXiv  Detail & Related papers  (2021-01-18T22:53:29Z)
• VAEs in the Presence of Missing Data [6.397263087026567]
  We develop a novel latent variable model of a corruption process which generates missing data, and derive a corresponding tractable evidence lower bound (ELBO) Our model is straightforward to implement, can handle both missing completely at random (MCAR) and missing not at random (MNAR) data, scales to high dimensional inputs and gives both the VAE encoder and decoder access to indicator variables for whether a data element is missing or not. On the MNIST and SVHN datasets we demonstrate improved marginal log-likelihood of observed data and better missing data imputation, compared to existing approaches.
  arXiv  Detail & Related papers  (2020-06-09T14:40:00Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)
• Multiple Imputation for Biomedical Data using Monte Carlo Dropout Autoencoders [0.0]
  This paper presents an approach based on Monte Carlo dropout within (Variational) Autoencoders. The evaluation shows that the imputation error and predictive similarity can be improved with the proposed approach.
  arXiv  Detail & Related papers  (2020-05-13T06:28:13Z)

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.