Multi-task longitudinal forecasting with missing values on Alzheimer's Disease

• URL: http://arxiv.org/abs/2201.05040v1
• Date: Thu, 13 Jan 2022 16:02:35 GMT
• Title: Multi-task longitudinal forecasting with missing values on Alzheimer's Disease
• Authors: Carlos Sevilla-Salcedo, Vandad Imani, Pablo M. Olmos, Vanessa G\'omez-Verdejo, Jussi Tohka
• Abstract summary: We propose a framework using the recently presented SSHIBA model for jointly learning different tasks on longitudinal data with missing values. The method uses Bayesian variational inference to impute missing values and combine information of several views. We apply this model to predict together diagnosis, ventricle volume, and clinical scores in dementia.
• Score: 4.5855304767722185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning techniques typically applied to dementia forecasting lack in their capabilities to jointly learn several tasks, handle time dependent heterogeneous data and missing values. In this paper, we propose a framework using the recently presented SSHIBA model for jointly learning different tasks on longitudinal data with missing values. The method uses Bayesian variational inference to impute missing values and combine information of several views. This way, we can combine different data-views from different time-points in a common latent space and learn the relations between each time-point while simultaneously modelling and predicting several output variables. We apply this model to predict together diagnosis, ventricle volume, and clinical scores in dementia. The results demonstrate that SSHIBA is capable of learning a good imputation of the missing values and outperforming the baselines while simultaneously predicting three different tasks.

Related papers

• A Unified Model for Longitudinal Multi-Modal Multi-View Prediction with Missingness [25.95298616599799]
  We introduce a unified model for longitudinal multi-modal multi-view prediction with missingness. Our method allows as many timepoints as desired for input, and aims to leverage all available data, regardless of their availability. We conduct extensive experiments on the knee osteoarthritis dataset from the Osteoarthritis Initiative for pain and Kellgren-Lawrence grade prediction at a future timepoint.
  arXiv  Detail & Related papers  (2024-03-18T19:51:55Z)
• Comparative study of clustering models for multivariate time series from connected medical devices [0.0]
  We show that a predictive model can be used to predict future values while forming a latent cluster space. We compare two models on Withing's datasets, M AGMAC LUST which clusters entire time series and DGM$2$ which allows the group affiliation of an individual to change over time.
  arXiv  Detail & Related papers  (2023-12-28T07:37:30Z)
• Time Associated Meta Learning for Clinical Prediction [78.99422473394029]
  We propose a novel time associated meta learning (TAML) method to make effective predictions at multiple future time points. To address the sparsity problem after task splitting, TAML employs a temporal information sharing strategy to augment the number of positive samples. We demonstrate the effectiveness of TAML on multiple clinical datasets, where it consistently outperforms a range of strong baselines.
  arXiv  Detail & Related papers  (2023-03-05T03:54:54Z)
• Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations [84.42837346400151]
  Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare. Existing causal inference approaches consider regular, discrete-time intervals between observations and treatment decisions. We propose a controllable simulation environment based on a model of tumor growth for a range of scenarios.
  arXiv  Detail & Related papers  (2022-06-16T17:15:15Z)
• 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)
• Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition [65.77962788209103]
  We propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge. It enforces the model to focus on learning the subset-specific knowledge. The proposed framework proved to be effective for the long-tailed retinal diseases recognition task.
  arXiv  Detail & Related papers  (2021-04-22T13:39:33Z)
• Multi-view Integration Learning for Irregularly-sampled Clinical Time Series [1.9639092030562577]
  We propose a multi-view features integration learning from irregular time series data by self-attention mechanism in an imputation-free manner. We explicitly learn the relationships among the observed values, missing indicators, and time interval between the consecutive observations, simultaneously. We build an attention-based decoder as a missing value imputer that helps empower the representation learning of the inter-relations among multi-view observations.
  arXiv  Detail & Related papers  (2021-01-25T10:02:50Z)
• Longitudinal Variational Autoencoder [1.4680035572775534]
  A common approach to analyse high-dimensional data that contains missing values is to learn a low-dimensional representation using variational autoencoders (VAEs) Standard VAEs assume that the learnt representations are i.i.d., and fail to capture the correlations between the data samples. We propose the Longitudinal VAE (L-VAE), that uses a multi-output additive Gaussian process (GP) prior to extend the VAE's capability to learn structured low-dimensional representations. Our approach can simultaneously accommodate both time-varying shared and random effects, produce structured low-dimensional representations
  arXiv  Detail & Related papers  (2020-06-17T10:30:14Z)
• Temporal Phenotyping using Deep Predictive Clustering of Disease Progression [97.88605060346455]
  We develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest. Experiments on two real-world datasets show that our model achieves superior clustering performance over state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2020-06-15T20:48:43Z)
• Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units [1.0349800230036503]
  We present a model for uncovering interactions with the highest likelihood of generating the outcomes seen from highly-dimensional time series data. Using the assumption that similar templates of small interactions are responsible for the outcomes, we reformulate the discovery task to retrieve the most-likely templates from the data.
  arXiv  Detail & Related papers  (2020-04-03T08:49:40Z)
• Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models [110.82452096672182]
  We propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data. We also introduce a novel metric for ambiguous problems, which is better suited to account for uncertainties.
  arXiv  Detail & Related papers  (2020-03-10T09:15:42Z)

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.