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A Stochastic Multivariate Irregularly Sampled Time Series Imputation Method for Electronic Health Records

Muhammad Adib Uz Zaman, Dongping Du
2021 BioMedInformatics  
To build an efficient predictive model, a complete dataset is necessary. An EHR usually contains high-dimensional longitudinal time series data.  ...  observed time series measurements in high-dimensional EHR data.  ...  However, that approach is more appropriate for continuous time series modeling with regularly sampled data.  ... 
doi:10.3390/biomedinformatics1030011 fatcat:d2xfdo3ervbzbczo5biclo5yai

Latent Ordinary Differential Equations for Irregularly-Sampled Time Series

Yulia Rubanova, Tian Qi Chen, David Duvenaud
2019 Neural Information Processing Systems  
We show experimentally that these ODE-based models outperform their RNN-based counterparts on irregularly-sampled data.  ...  Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs).  ...  Acknowledgments We thank Chun-Hao Chang, Chris Cremer, Quaid Morris, and Ladislav Rampasek for helpful discussions and feedback. We thank the Vector Institute for providing computational resources.  ... 
dblp:conf/nips/RubanovaCD19 fatcat:ahq34rsdrnct7k2sxyn4yytlwe

Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping [article]

Michael Moor and Max Horn and Bastian Rieck and Damian Roqueiro and Karsten Borgwardt
2020 arXiv   pre-print
Our deep learning model employs a temporal convolutional network that is embedded in a Multi-task Gaussian Process Adapter framework, making it directly applicable to irregularly-spaced time series data  ...  This empirical study proposes two novel approaches for the early detection of sepsis: a deep learning model and a lazy learner based on time series distances.  ...  For this study, we focused on irregularly sampled time series data comprising vital and laboratory parameters.  ... 
arXiv:1902.01659v4 fatcat:24b4iwpih5d5rijci4chl5ybie

Bayesian analysis of ambulatory blood pressure dynamics with application to irregularly spaced sparse data

Zhao-Hua Lu, Sy-Miin Chow, Andrew Sherwood, Hongtu Zhu
2015 Annals of Applied Statistics  
The MR algorithm can produce more efficient MCMC samples that is crucial for valid parameter estimation and inference.  ...  To circumvent the data analytic constraint that empirical measurements are typically collected at irregular and much larger time intervals than those evaluated in simulation studies of SDEs, we adopt a  ...  The MR approach is a mixture of a series of local samplers and a global sampler, and generates samples for every resolution sequentially.  ... 
doi:10.1214/15-aoas846 pmid:26941885 pmcid:PMC4773035 fatcat:fzjcvi6ji5fyde4g2p5z574xt4

Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks

Jinsung Yoon, William R. Zame, Mihaela van der Schaar
2018 International Conference on Learning Representations  
For every prediction we might wish to make, we must decide what to observe (what source of information) and when to observe it.  ...  At training time, Deep Sensing learns how to issue predictions at various cost-performance points.  ...  ACKNOWLEDGMENTS This work was supported by the Office of Naval Research (ONR) and the NSF (Grant number: ECCS1462245, ECCS1533983, and ECCS1407712).  ... 
dblp:conf/iclr/YoonZS18 fatcat:pchgux25l5djneeya4c75s3taq

Clinical time series prediction: Toward a hierarchical dynamical system framework

Zitao Liu, Milos Hauskrecht
2015 Artificial Intelligence in Medicine  
Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive  ...  In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations.  ...  Acknowledgments This research work was supported by grants R01LM010019 and R01GM088224 from the NIH.  ... 
doi:10.1016/j.artmed.2014.10.005 pmid:25534671 pmcid:PMC4422790 fatcat:2c3mq5hajfac5m3f4o6iq633yu

Marked Point Process for Severity of Illness Assessment

Kazi T. Islam, Christian R. Shelton, Juan I. Casse, Randall Wetzel
2017 Machine Learning in Health Care  
Such data can be treated as a temporal stream of events of varied types occurring at irregularly spaced time points.  ...  Electronic Health Records (EHRs) consist of sparse, noisy, incomplete, heterogeneous and unevenly sampled clinical data of patients.  ...  Saria et al. (2010) focused on a non-parametric Bayesian method for data analysis in continuous time series including health care data, based on topic models.  ... 
dblp:conf/mlhc/IslamSCW17 fatcat:6ynlugzm35dgphr3gprgznslzu

Data imputation in a short-run space-time series: A Bayesian approach

Lars Pforte, Chris Brunsdon, Conor Cahalane, Martin Charlton
2017 Environment and Planning B Urban Analytics and City Science  
Furthermore, we explain how we achieved spatial coherence between different time series and their observed and estimated data points.  ...  Thus the database consists of various time series with a spatial component. As a substantial amount of the data was missing a method of imputation was required to complete the database.  ...  Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors gratefully acknowledge support from the ESPON Programme  ... 
doi:10.1177/0265813516688688 fatcat:wjpxinpuu5gyvdd4t47fkmkpha

Modern Multiple Imputation with Functional Data [article]

Aniruddha Rajendra Rao, Matthew Reimherr
2020 arXiv   pre-print
This work considers the problem of fitting functional models with sparsely and irregularly sampled functional data.  ...  We also propose a new imputation approach that combines the ideas of MissForest with Local Linear Forest and compare their performance with PACE and several other multivariate multiple imputation methods  ...  Table 1: RMSE of Prediction, β coefficients and Imputation of the curves for different methods under Linear case when n=500 for different time points and sparsity settings.  ... 
arXiv:2011.12509v1 fatcat:kpjlwvt43jfidg4lw4fr5n3dei

TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series [article]

Yang Jiao, Kai Yang, Shaoyu Dou, Pan Luo, Sijia Liu, Dongjin Song
2020 arXiv   pre-print
To this end, we propose an autonomous representation learning approach for multivariate time series (TimeAutoML) with irregular sampling rates and variable lengths.  ...  Multivariate time series (MTS) data are becoming increasingly ubiquitous in diverse domains, e.g., IoT systems, health informatics, and 5G networks.  ...  Irregularly Sampled Time Series Learning There exist two main groups of works regarding machine learning for irregularly sampled time series data.  ... 
arXiv:2010.01596v1 fatcat:hjd4muih7zdhdophs7hrf5g57m

Clairvoyance: A Pipeline Toolkit for Medical Time Series

Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar
2021 International Conference on Learning Representations  
At the same time, medical time-series problems in the wild are challenging due to their highly composite nature: They entail design choices and interactions among components that preprocess data, impute  ...  Time-series learning is the bread and butter of data-driven clinical decision support, and the recent explosion in ML research has demonstrated great potential in various healthcare settings.  ...  ACKNOWLEDGMENTS We would like to thank the reviewers for their generous and invaluable comments and suggestions.  ... 
dblp:conf/iclr/JarrettYBQES21 fatcat:ltp7ijb24rhqnccpby74cooflq

GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series [article]

Edward De Brouwer and Jaak Simm and Adam Arany and Yves Moreau
2019 arXiv   pre-print
Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i.e., sampling is irregular both in time and across dimensions)-such as in the case  ...  What is more, the continuity prior is shown to be well suited for low number of samples settings.  ...  Acknowledgements We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. † Statistically not different from best (p-value > 0.6 with  ... 
arXiv:1905.12374v2 fatcat:cr3vfswbwrfd7kukvico3r5h2e

Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction [article]

Hossein Soleimani, James Hensman, Suchi Saria
2017 arXiv   pre-print
Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data.  ...  Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty due to missingness.  ...  ACKNOWLEDGMENTS The authors thank Wenbo Pan for his help with an initial implementation of the algorithm and Katharine Henry and Andong Zhang for their help with data.  ... 
arXiv:1708.04757v1 fatcat:3luywvmi5jaejbph4c34z526ey

Bayesian Prediction of Severe Outcomes in the LabMarCS: Laboratory Markers of COVID-19 Severity - Bristol Cohort [article]

Brian Sullivan, Edward Barker, Philip Williams, Louis MacGregor, Ranjeet Bhamber, Matt Thomas, Stefan Gurney, Catherine Hyams, Alastair Whiteway, Jennifer A Cooper, Chris McWilliams, Katy Turner (+2 others)
2022 medRxiv   pre-print
Objectives: To develop cross-validated prediction models for severe outcomes in COVID-19 using blood biomarker and demographic data; Demonstrate best practices for clinical data curation and statistical  ...  Variable selection performed using Bayesian predictive projection determined a four variable model using Age, Urea, Prothrombin time and Neutrophil-Lymphocyte ratio, with a median AUC of 0.74 [0.67, 0.82  ...  Acknowledgements This research was supported by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration West (NIHR ARC West).  ... 
doi:10.1101/2022.09.16.22279985 fatcat:w5fajbm23be4fj5yhwqhd2ogiq

Temporal Convolutional Neural Networks for Diagnosis from Lab Tests [article]

Narges Razavian, David Sontag
2016 arXiv   pre-print
Our novel architecture takes as input both an imputed version of the data and a binary observation matrix.  ...  Early diagnosis of treatable diseases is essential for improving healthcare, and many diseases' onsets are predictable from annual lab tests and their temporal trends.  ...  The Tesla K40s used for this research were donated by the NVIDIA Corporation.  ... 
arXiv:1511.07938v4 fatcat:sveroidxuzbnhgcys32vgtubg4
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