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Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series
[article]
2020
arXiv
pre-print
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In this work, we propose a novel variational-recurrent imputation network, which unifies an imputation and a prediction network by taking into account the correlated features, temporal dynamics, as well ...
Specifically, we leverage the deep generative model in the imputation, which is based on the distribution among variables, and a recurrent imputation network to exploit the temporal relations, in conjunction ...
We propose a novel variational-recurrent imputation network (V-RIN) which unifies the imputation and prediction networks for multivariate time series EHR data, governing both correlations among variables ...
arXiv:2003.00662v2
fatcat:gkm463rpangm5l7fx7itmaocye
Path Imputation Strategies for Signature Models of Irregular Time Series
[article]
2020
arXiv
pre-print
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Next, we observe that uncertainty-aware predictions (based on GP-PoM or indicator imputations) are beneficial for predictive performance, even compared to the uncertainty-aware training of conventional ...
The signature transform is a 'universal nonlinearity' on the space of continuous vector-valued paths, and has received attention for use in machine learning on time series. ...
Overall, we find that uncertainty-aware approaches (indicator imputation and GP-PoM) are beneficial when imputing irregularly-spaced time series for classification. ...
arXiv:2005.12359v2
fatcat:z2kcwx545zgd3jv2twb54r7flu
Multi-view Integration Learning for Irregularly-sampled Clinical Time Series
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2101.09986v1 fatcat:3czlymxa6bh45etu5tqajwwge4
In this work, we propose a multi-view features integration learning from irregular multivariate time series data by self-attention mechanism in an imputation-free manner. ...
Electronic health record (EHR) data is sparse and irregular as it is recorded at irregular time intervals, and different clinical variables are measured at each observation point. ...
The classical machine learning approaches for handling the irregularly sampled time series are mostly based on convolutional neural network (CNN), recurrent neural network (RNN), and more recently attention-based ...
arXiv:2101.09986v2
fatcat:u46qtifgbjer3odmjtmu3ynxfq
Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series
[article]
2022
arXiv
pre-print
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Experiments on real-world multivariate clinical time-series benchmark datasets demonstrate that STraTS has better prediction performance than state-of-the-art methods for mortality prediction, especially ...
To tackle this problem, we propose a Self-supervised Transformer for Time-Series (STraTS) model which overcomes these pitfalls by treating time-series as a set of observation triplets instead of using ...
ACKNOWLEDGMENTS We thank Lakshmi Tipirneni for her help with the clinical domain knowledge related to the extraction of our time-series dataset from MIMIC-III database and for providing clinical insights ...
arXiv:2107.14293v2
fatcat:bzcfmzb55jcxjb2rnaj2kbrfji
Predictive Analytics for Caring and Managing Acute Disease Patients: A Deep Learning–Based Method to Predict Crucial Complications Phenotypes (Preprint)
2020
Journal of Medical Internet Research
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insufficiencies and account for variations in phenotypic expressions. ...
To predict crucial complication phenotypes among patients with acute diseases, we propose a novel, deep learning-based method that uses recurrent neural network-based sequence embedding to represent disease ...
Acknowledgments The authors thank the Research Institute of Chang Gung Memorial Hospital for their assistance in data collection and preprocessing. ...
doi:10.2196/18372
pmid:33576744
fatcat:atowfq2ylnhu5alcidkqdhnmva
Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier
[article]
2017
arXiv
pre-print
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Our proposed framework models the multivariate trajectories of continuous-valued physiological time series using multitask Gaussian processes, seamlessly accounting for the high uncertainty, frequent missingness ...
The Gaussian process is directly connected to a black-box classifier that predicts whether a patient will become septic, chosen in our case to be a recurrent neural network to account for the extreme variability ...
This project was partially funded by the Duke Institute for Health Innovation. ...
arXiv:1706.04152v1
fatcat:lzyiwzcp4fgthm2dabansbiium
Quality control, data cleaning, imputation
[article]
2021
arXiv
pre-print
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A gentle introduction is provided on common statistical and machine learning methods for imputation. ...
Finally, we introduce alternative methods to address incomplete data without the need for imputation. ...
A first type of RNN methods generate multiple imputed datasets, and include Bidirectional Recurrent Imputation for Time Series (117) , multi-directional recurrent neural networks (118) , and residual ...
arXiv:2110.15877v1
fatcat:tzswjt4kerd7zegbgt5mdm23gi
Temporal Clustering with External Memory Network for Disease Progression Modeling
[article]
2021
arXiv
pre-print
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To address these two issues, we propose Temporal Clustering with External Memory Network (TC-EMNet) for DPM that groups patients with similar trajectories to form disease clusters/stages. ...
for producing comprehensive patient states. ...
[22] proposed to use VAE to impute missing values for electronic health data with uncertainty-aware attention. ...
arXiv:2109.14147v2
fatcat:tjm4eqplnvaapfel44dbv72lme
Probabilistic Machine Learning for Healthcare
[article]
2020
arXiv
pre-print
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Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning. ...
Krishnan, Peter Schulam, and Pete Szolovits for helpful and useful feedback. This work was supported in part by a CIFAR AI Chair at the Vector Institute (MG) and Microsoft Research (MG). ...
Using these indicators of observation in a time series, recurrent neural networks have been used to predict patient outcomes in the intensive care unit (11) . ...
arXiv:2009.11087v1
fatcat:htosfeqvhndvfmlmud2pvl3nsy
AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units
[article]
2021
arXiv
pre-print
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In this work, we propose a novel generative deep probabilistic model for real-time risk scoring in ICUs. Specifically, we develop an attentive deep Markov model called AttDMM. ...
Clinical practice in intensive care units (ICUs) requires early warnings when a patient's condition is about to deteriorate so that preventive measures can be undertaken. ...
70.04
Systolic blood pressure
time-series
49.36
Heart rate
time-series
48.69
Body temperature
time-series
70.46
PaO 2 /FiO 2
time-series
93.88
Urinary output
time-series
57.16
Serum urea ...
arXiv:2102.04702v2
fatcat:3rhtfxfocnavvkipwlse5x2gbm
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction
[article]
2017
arXiv
pre-print
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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. ...
Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. ...
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
A Multi-Agent Approach for Personalized Hypertension Risk Prediction
2021
IEEE Access
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The time series is divided into time bins corresponding to the total number of seasonal variations that can be expected in the time series. ...
Therefore, we utilize the same approach to model this seasonal variation in the blood pressure data and detect any outliers in the time series. ...
doi:10.1109/access.2021.3074791
fatcat:wanpehawkbgutge5aunktmwzcu
Systematic Review of Using Machine Learning in Imputing Missing Values
2022
IEEE Access
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The authors would like to thank the anonymous reviewers for their comments. ...
For instance, recurrent neural networks are usually utilized to address missing values in longitudinal or time-series datasets [108] . ...
Different datasets have different data types, such as time-series datasets, which work better with recurrent neural networks [108] . ...
doi:10.1109/access.2022.3160841
fatcat:uxrqlpsdaretrhbnccibqwzvi4
SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Deep Vein Thrombosis (DVT) prediction
[article]
2022
arXiv
pre-print
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To that end, we propose a generative time-to-event model, SurvLatent ODE, which adopts an Ordinary Differential Equation-based Recurrent Neural Networks (ODE-RNN) as an encoder to effectively parameterize ...
SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying DVT risk groups. ...
Mayer Foundation, DDCF Clinical Scientist Development Award, Phi Beta Psi Sorority, and The Emerson Collective. ...
arXiv:2204.09633v1
fatcat:blc3ubyjvnb25mnhylacv35vey
Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects
2022
Sensors
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Clinical studies employing wearable devices are also increasing. In this article, we review the nature of the wearables-associated data and the downstream processing methods for detecting anomalies. ...
Given the clinical significance of anomalies and their impact on diagnosis and treatment, a wide range of detection methods have been proposed to detect anomalies. ...
Acknowledgments: The authors would like to thank Manasa Tata and Omnia Heikal from Rhenix Lifesciences for their contributions in reviewing the manuscript and preparing the figures, respectively. ...
doi:10.3390/s22030756
pmid:35161502
pmcid:PMC8840097
fatcat:nlju2m7btjccxk6xz6tfl6jyt4
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