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We present a comprehensive analysis of deep learning approaches for Electronic Health Record (EHR) time-series imputation, examining how architectural and framework biases combine to influence model performance.
Dynamic programming and Markov processes
R.A. Howard · 1960
Earlier work this paper cites.
Stochastic differential equations
Nicolaas G Van Kampen · 1976
Earlier work this paper cites.
Introduction to gaussian processes
David JC MacKay et al · 1998
Earlier work this paper cites.
Recurrent neural networks
Larry R Medsker and LC Jain · 2001
Earlier work this paper cites.
Medical temporal-knowledge discovery via temporal abstraction
Robert Moskovitch and Yuval Shahar · 2009
Earlier work this paper cites.
Pattern classification with missing data: a review
Pedro J García-Laencina, José-Luis Sancho-Gómez, and Aníbal R Figueiras-Vidal · 2010
Earlier work this paper cites.
Mining electronic health records: towards better research applications and clinical care
Peter B Jensen, Lars J Jensen, and Søren Brunak · 2012
Earlier work this paper cites.
Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012
Ikaro Silva, George Moody, Daniel Scott, Leo Celi, and Roger Mark · 2012
Earlier work this paper cites.
Temporal trends of hemoglobin a1c testing
Rimma Pivovarov, David J Albers, George Hripcsak, Jorge L Sepulveda, and Noémie Elhadad · 2014
Earlier work this paper cites.
Identifying and mitigating biases in ehr laboratory tests
Rimma Pivovarov, David J Albers, Jorge L Sepulveda, and Noémie Elhadad · 2014
Earlier work this paper cites.
Imaging time-series to improve classification and imputation
Zhiguang Wang and Tim Oates · 2015
Earlier work this paper cites.
A hybrid method for interpolating missing data in heterogeneous spatio-temporal datasets
Min Deng, Zide Fan, Qiliang Liu, and Jianya Gong · 2016
Earlier work this paper cites.
Mimic-iii, a freely accessible critical care database
Alistair Johnson and et al · 2016
Earlier work this paper cites.
Continuous glucose monitoring: a review of successes, challenges, and opportunities
David Rodbard · 2016
Earlier work this paper cites.
Machine learning landscapes and predictions for patient outcomes
Ritankar Das and David J. Wales · 2017
Earlier work this paper cites.
Continuous vital sign analysis for predicting and preventing noncardiac complications after major surgery
Travis J Moss, Douglas E Lake, J Forrest Calland, Kyle B Enfield, John B Delos, Karen D Fairchild, and J Randall Moorman · 2017
Earlier work this paper cites.
Vigan: Missing view imputation with generative adversarial networks
Chao Shang, Aaron Palmer, Jiangwen Sun, Ko-Shin Chen, Jin Lu, and Jinbo Bi · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Multi-directional recurrent neural networks: A novel method for estimating missing data
Jinsung Yoon, William R Zame, and Mihaela van der Schaar · 2017
Earlier work this paper cites.
Mechanistic machine learning: how data assimilation leverages physiologic knowledge using bayesian inference to forecast the future, infer the present, and phenotype
David J Albers, Matthew E Levine, Andrew Stuart, Lena Mamykina, Bruce Gluckman, and George Hripcsak · 2018
Earlier work this paper cites.
Brits: Bidirectional recurrent imputation for time series
Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li · 2018
Earlier work this paper cites.
Recurrent neural networks for multivariate time series with missing values
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu · 2018
Earlier work this paper cites.
Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
Earlier work this paper cites.
Generative adversarial networks: An overview
Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, and Anil A Bharath · 2018
Earlier work this paper cites.
Deep learning in the medical domain: Predicting cardiac arrest using deep learning
Youngjae Lee, Joon-myoung Kwon, Yeha Lee, Hyunho Park, Hyungchul Cho, and Jinsik Park · 2018
Earlier work this paper cites.
Deep learning in the medical domain: Predicting cardiac arrest using deep learning
Youngjae Lee, Joon-myoung Kwon, Yeha Lee, Hyunho Park, Hyungchul Cho, and Jinsik Park · 2018
Earlier work this paper cites.
Multivariate time series imputation with generative adversarial networks
Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu, et al · 2018
Earlier work this paper cites.
Deep learning for healthcare: review, opportunities and challenges
R. Miotto, F. Wang, S. Wang, X. Jiang, and J.T. Dudley · 2018
Earlier work this paper cites.
Artificial intelligence (ai) and global health: how can ai contribute to health in resource-poor settings?
Brian Wahl, Aline Cossy-Gantner, Stefan Germann, and Nina R Schwalbe · 2018
Earlier work this paper cites.
The incidence of cardiac arrest in the intensive care unit: A systematic review and meta-analysis
Richard A Armstrong, Caroline Kane, Fiona Oglesby, Katie Barnard, Jasmeet Soar, and Matt Thomas · 2019
Earlier work this paper cites.
Naomi: Non-autoregressive multiresolution sequence imputation
Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, and Yisong Yue · 2019
Earlier work this paper cites.
E2gan: End-to-end generative adversarial network for multivariate time series imputation
Yonghong Luo, Ying Zhang, Xiangrui Cai, and Xiaojie Yuan · 2019
Earlier work this paper cites.
Miwae: Deep generative modelling and imputation of incomplete data sets
Pierre-Alexandre Mattei and Jes Frellsen · 2019
Cited alongside, same era.
Latent ordinary differential equations for irregularly-sampled time series
Yulia Rubanova, Ricky TQ Chen, and David K Duvenaud · 2019
Cited alongside, same era.
Fourth universal definition of myocardial infarction (2018)
Kristian Thygesen, Joseph S Alpert, Allan S Jaffe, Bernard R Chaitman, Jeroen J Bax, David A Morrow, and Harvey D White · 2019
Cited alongside, same era.
Multivariate time series forecasting via attention-based encoder–decoder framework
Shengdong Du, Tianrui Li, Yan Yang, and Shi-Jinn Horng · 2020
Cited alongside, same era.
GP-VAE: Deep probabilistic time series imputation
Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, and Stephan Mandt · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Deepmpm: a mortality risk prediction model using longitudinal ehr data
Fan Yang, Jian Zhang, Wanyi Chen, Yongxuan Lai, Ying Wang, and Quan Zou · 2022
Later among the works it cites.
Multivariate time series imputation with transformers
A Yarkın Yıldız, Emirhan Koç, and Aykut Koç · 2022
Later among the works it cites.
Deep learning prediction models based on ehr trajectories: A systematic review
Ali Amirahmadi, Mattias Ohlsson, and Kobra Etminani · 2023
Later among the works it cites.
Outcome class imbalance and rare events: An underappreciated complication for overdose risk prediction modeling
Adrian R. Cartus, Elliot A. Samuels, Magdalena Cerdá, and Brandon D. L. Marshall · 2023
Later among the works it cites.
Tsmixer: An all-mlp architecture for time series forecasting
Si-An Chen, Chun-Liang Li, Nate Yoder, Sercan O Arik, and Tomas Pfister · 2023
Later among the works it cites.
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Handling incomplete heterogeneous data using vaes
Alfredo Nazabal, Pablo M Olmos, Zoubin Ghahramani, and Isabel Valera · 2020
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Deep representation learning of patient data from electronic health records (ehr): A systematic review
Yuqi Si, Jingcheng Du, Zhao Li, Xiaoqian Jiang, Timothy A. Miller, Fei Wang, W. Jim Zheng, and Kirk Roberts · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Rethinking 1d-cnn for time series classification: A stronger baseline
Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Jing Jiang, and Michael Blumenstein · 2020
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Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang · 2020
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Provably convergent schrödinger bridge with applications to probabilistic time series imputation
Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, and Yuriy Nevmyvaka · 2023
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Rdis: Random drop imputation with self-training for incomplete time series data
Tae-Min Choi, Ji-Su Kang, and Jong-Hwan Kim · 2023
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Pypots: A python toolbox for data mining on partially-observed time series
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Saits: Self-attention-based imputation for time series
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Machine learning-based early prediction of sepsis using electronic health records: A systematic review
Khandaker Reajul Islam, Johayra Prithula, Jaya Kumar, Toh Leong Tan, Mamun Bin Ibne Reaz, Md. Shaheenur Islam Sumon, and Muhammad E. H. Chowdhury · 2023
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Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I Webb, Irwin King, and Shirui Pan · 2023
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Deep imputation of missing values in time series health data: A review with benchmarking
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Probabilistic imputation for time-series classification with missing data
SeungHyun Kim, Hyunsu Kim, Eunggu Yun, Hwangrae Lee, Jaehun Lee, and Juho Lee · 2023
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Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques
Mingxuan Liu, Siqi Li, Han Yuan, Marcus Eng Hock Ong, Yilin Ning, Feng Xie, Seyed Ehsan Saffari, Yuqing Shang, Victor Volovici, Bibhas Chakraborty, and Nan Liu · 2023
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Learning from data with structured missingness
Robin Mitra, Sarah F McGough, Tapabrata Chakraborti, Chris Holmes, Ryan Copping, Niels Hagenbuch, Stefanie Biedermann, Jack Noonan, Brieuc Lehmann, Aditi Shenvi, et al · 2023
Later among the works it cites.
Addressing class imbalance in electronic health records data imputation
Linglong Qian, Zina M. Ibrahim, Ao Zhang, and Richard J. B. Dobson · 2023
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Non-autoregressive conditional diffusion models for time series prediction
Lifeng Shen and James Kwok · 2023
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Yet another icu benchmark: A flexible multi-center framework for clinical ml
Robin van de Water, Hendrik Schmidt, Paul Elbers, Patrick Thoral, Bert Arnrich, and Patrick Rockenschaub · 2023
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Timesnet: Temporal 2d-variation modeling for general time series analysis
Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long · 2023
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Density-aware temporal attentive step-wise diffusion model for medical time series imputation
Jingwen Xu, Fei Lyu, and Pong C Yuen · 2023
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Are transformers effective for time series forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu · 2023
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A comprehensive survey on deep learning for missing data imputation: Taxonomy, challenges, and future directions
Yongshun Zhang, Xinhang Li, Zehong Li, Wei Liu, Zhihua Zhang, and Jing Liu · 2023
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Deep learning using 2d convolutional neural networks for multivariate clinical time series: A review and comparison
Min Zhao, Jianqing Yang, Wei Wang, Xiaojuan Ma, and Zaiqing Nie · 2023
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Missing data matter: an empirical evaluation of the impacts of missing ehr data in comparative effectiveness research
Y. Zhou, J. Shi, R. Stein, X. Liu, R. N. Baldassano, C. B. Forrest, Y. Chen, and J. Huang · 2023
Later among the works it cites.
On mixture density networks: A survey
Ningning Zhuang, Mengmeng Gong, Jiahui Zhu, Yilong Yin, Bo Liu, and Jun Wang · 2023
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Impact of a deep learning sepsis prediction model on quality of care and survival
Aaron Boussina, Supreeth P. Shashikumar, Atul Malhotra, Robert L. Owens, Robert El-Kareh, Christopher A. Longhurst, Kimberly Quintero, Allison Donahue, Theodore C. Chan, Shamim Nemati, and Gabriel Wardi · 2024
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