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Irregularly sampled time series (ISTS) data has irregular temporal intervals between observations and different sampling rates between sequences.
Singular value decomposition and least squares solutions
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Long short-term memory
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Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals
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Shahla Parveen and Phil D. Green · 2001
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Kalman Filtering and Neural Networks
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Wavelet methods for time series analysis. (book reviews)
Todd Ogden · 2002
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Framewise phoneme classification with bidirectional lstm and other neural network architectures
Alex Graves and Jürgen Schmidhuber · 2005
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The effects of the irregular sample and missing data in time series analysis
David M. Kreindler and Charles J. Lumsden · 2006
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An efficient nearest neighbor classifier algorithm based on pre-classify
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A modified svm classifier based on rs in medical disease prediction
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Arterial blood pressure during early sepsis and outcome
Martin W. Dünser, Jukka Takala, Hanno Ulmer, Viktoria D. Mayr, Günter Luckner, Stefan Jochberger, Fritz Daudel, Philipp Lepper, Walter R. Hasibeder, and Stephan M. Jakob · 2009
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Spectral regularization algorithms for learning large incomplete matrices
Rahul Mazumder, Trevor Hastie, and Robert Tibshirani · 2009
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Time series analysis : forecasting and control
G. E. P. Box and G. M. Jenkins · 2010
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Pattern classification with missing data: a review
Pedro J. García-Laencina, José-Luis Sancho-Gómez, and Aníbal R. Figueiras-Vidal · 2010
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Strategies for handling missing data in electronic health record derived data
Amy S Nowacki Brian J Wells, Kevin M Chagin and Michael W Kattan · 2010
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A survey of methodologies for the treatment of missing values within datasets: limitations and benefits
W. Young, G. Weckman, and W. Holland · 2011
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Predicting disease risks from highly imbalanced data using random forest
Mohammed Khalilia, Sounak Chakraborty, and Mihail Popescu · 2011
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Comparison of correlation analysis techniques for irregularly sampled time series
Rehfeld, K., Marwan, N., Heitzig, J., Kurths, and J · 2011
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Multiple imputation using chained equations. issues and guidance for practice
Patrick Royston Ian R White and Angela M Wood · 2011
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Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012
Ivanovitch Silva, Galan Moody, Daniel J Scott, Leo A Celi, and Roger G Mark · 2012
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Survey of clinical data mining applications on big data in health informatics
Matthew Herland, Taghi M. Khoshgoftaar, and Randall Wald · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, Kyung Hyun Cho, and Yoshua Bengio · 2014
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A learning algorithm for continually running fully recurrent neural networks
R Williams and D Zipser · 2014
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Hospital deaths in patients with sepsis from 2 independent cohorts
Vincent Liu, Gabriel J. Escobar, John D. Greene, Jay Soule, Alan Whippy, Derek C. Angus, and Theodore J. Iwashyna · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, and David Warde-Farley · 2014
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Convolutional LSTM network: A machine learning approach for precipitation nowcasting
Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo · 2015
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Deep computational phenotyping
Zhengping Che, David C. Kale, Wenzhe Li, Mohammad Taha Bahadori, and Yan Liu · 2015
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Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis
Adler J. Perotte, Rajesh Ranganath, Jamie S. Hirsch, David M. Blei, and Noémie Elhadad · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Learning to diagnose with LSTM recurrent neural networks
Zachary Chase Lipton, David C. Kale, Charles Elkan, and Randall C. Wetzel · 2016
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RETAIN: an interpretable predictive model for healthcare using reverse time attention mechanism
Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter F. Stewart · 2016
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Multi-layer representation learning for medical concepts
Edward Choi, Mohammad Taha Bahadori, Elizabeth Searles, Catherine Coffey, Michael Thompson, James Bost, Javier Tejedor-Sojo, and Jimeng Sun · 2016
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Directly modeling missing data in sequences with rnns: Improved classification of clinical time series
Zachary C. Lipton, David C. Kale, and Randall C. Wetzel · 2016
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Mimic-iii, a freely accessible critical care database
A. et al. Johnson · 2016
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Analysis of incomplete and inconsistent clinical survey data
Suzan Arslanturk, Mohammad Reza Siadat, Theophilus Ogunyemi, Kim Killinger, and Ananias Diokno · 2016
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Data cleaning: Overview and emerging challenges
Xu Chu, Ihab F. Ilyas, Sanjay Krishnan, and Jiannan Wang · 2016
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A scalable end-to-end gaussian process adapter for irregularly sampled time series classification
Steven Cheng-Xian Li and Benjamin M. Marlin · 2016
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Doctor AI: predicting clinical events via recurrent neural networks
A distributed descriptor characterizing structural irregularity of EEG time series for epileptic seizure detection
Zhenning Mei, Xian Zhao, and Hongyu Chen · 2018
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Clustering and classification for time series data in visual analytics: A survey
M. Ali, A. Alqahtani, M. W. Jones, and X. Xie · 2019
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Adversarial unsupervised representation learning for activity time-series
Karan Aggarwal, Shafiq R. Joty, Luis Fernández-Luque, and Jaideep Srivastava · 2019
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Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction
Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, and Zhenhui Li · 2019
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Rnn-based longitudinal analysis for diagnosis of alzheimer’s disease
Ruoxuan Cui, Manhua Liu, and Alzheimer’s Disease Neuroimaging Initiative · 2019
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Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F. Stewart, and Jimeng Sun · 2016
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Temporal regularized matrix factorization for high-dimensional time series prediction
Hsiang-Fu Yu, Nikhil Rao, and Inderjit S. Dhillon · 2016
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Automatic classification of irregularly sampled time series with unequal lengths: A case study on estimated glomerular filtration rate
Santosh Tirunagari, Simon C. Bull, and Norman Poh · 2016
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Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks
Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, and Jing Gao · 2017
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Patient subtyping via time-aware lstm networks
Inci M. Baytas, Cao Xiao, Xi Zhang, Fei Wang, and Jiayu Zhou · 2017
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A deep learning method based on hybrid auto-encoder model
Yang Zhen Yu and Jing Hui · 2017
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An accurate saliency prediction method based on generative adversarial networks
Bing Yan, Haoqian Wang, Wang Xingzheng, and Zhang Yongbing · 2017
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Bidirectional recurrent auto-encoder for photoplethysmogram denoising
Joonnyong Lee, Sukkyu Sun, Seung Man Yang, Jang Jay Sohn, Jonghyun Park, Saram Lee, and Hee Chan Kim · 2019
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Research and application progress of generative adversarial networks
Mengting Chai and Yuanping Zhu · 2019
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Temporal-clustering invariance in irregular healthcare time series
Mohammad Taha Bahadori and Zachary Chase Lipton · 2019
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Interpolation-prediction networks for irregularly sampled time series
Satya Narayan Shukla and Benjamin M. Marlin · 2019
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An intelligent warning model for early prediction of cardiac arrest in sepsis patients
Samaneh Layeghian Javan, Mohammad Mehdi Sepehri, Malihe Layeghian Javan, and Toktam Khatibi · 2019
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K-margin-based residual-convolution-recurrent neural network for atrial fibrillation detection
Yuxi Zhou, Shenda Hong, Junyuan Shang, Meng Wu, Qingyun Wang, Hongyan Li, and Junqing Xie · 2019
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Early prediction of sepsis from clinical data – the physionet computing in cardiology challenge 2019 (version 1.0.0)
C. Reyna, M.and Josef, B.and Westover M. B. Jeter, R.and Shashikumar S.and Moody, Sharma A., Nemati S., and G. Clifford · 2019
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UA-CRNN: uncertainty-aware convolutional recurrent neural network for mortality risk prediction
Qingxiong Tan, Andy Jinhua Ma, Mang Ye, Baoyao Yang, Huiqi Deng, Vincent Wai-Sun Wong, Yee-Kit Tse, Terry Cheuk-Fung Yip, Grace Lai-Hung Wong, Jessica Yuet-Ling Ching, Francis Ka-Leung Chan, and Pong C. Yuen · 2019
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Improving missing data imputation with deep generative models
Ramiro Daniel Camino, Christian A. Hammerschmidt, and Radu State · 2019
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Misgan: Learning from incomplete data with generative adversarial networks
Steven Cheng-Xian Li, Bo Jiang, and Benjamin M. Marlin · 2019
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Mcpl-based FT-LSTM: medical representation learning-based clinical prediction model for time series events
Lutong Wang, Hong Wang, Yongqiang Song, and Qian Wang · 2019
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A comparison between discrete and continuous time bayesian networks in learning from clinical time series data with irregularity
Manxia Liu, Fabio Stella, Arjen Hommersom, Peter J. F. Lucas, Lonneke Boer, and Erik Bischoff · 2019
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Multi-resolution networks for flexible irregular time series modeling (multi-fit)
Bhanu Pratap Singh, Iman Deznabi, Bharath Narasimhan, Bryon Kucharski, Rheeya Uppaal, Akhila Josyula, and Madalina Fiterau · 2019
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Tensorized lstm with adaptive shared memory for learning trends in multivariate time series
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