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This study investigates the impact of masking strategies on time series imputation models in healthcare settings.
García-Laencina, P.J., Sancho-Gómez, J.L., Figueiras-Vidal, A.R.: Pattern classification with missing data: a review. Neural Computing and Applications 19
2010
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Silva, I., Moody, G., Mark, R., Celi, L.A.: Predicting mortality of icu patients: The physionet/computing in cardiology challenge 2012. Predicting Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge, p. v1 (2012)
2012
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Wells, B.J., Chagin, K.M., Nowacki, A.S., Kattan, M.W.: Strategies for handling missing data in electronic health record derived data. Egems 1
2013
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Yoon, J., Zame, W.R., van der Schaar, M.: Multi-directional recurrent neural networks: A novel method for estimating missing data. In: Time series workshop in international conference on machine learning (2017)
2017
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Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Brits: Bidirectional recurrent imputation for time series. Advances in neural information processing systems 31
2018
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Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports 8
2018
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Wahl, B., Cossy-Gantner, A., Germann, S., Schwalbe, N.R.: Artificial intelligence (ai) and global health: how can ai contribute to health in resource-poor settings? BMJ global health 3
2018
Cited alongside, same era.
Fortuin, V., Baranchuk, D., Rätsch, G., Mandt, S.: Gp-vae: Deep probabilistic time series imputation. In: International conference on artificial intelligence and statistics. pp. 1651–1661. PMLR (2020)
2020
Cited alongside, same era.
Miao, X., Wu, Y., Wang, J., Gao, Y., Mao, X., Yin, J.: Generative semi-supervised learning for multivariate time series imputation. In: Proceedings of the AAAI conference on artificial intelligence. vol. 35, pp. 8983–8991 (2021)
2021
Cited alongside, same era.
Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34
2021
Cited alongside, same era.
2023
Later among the works it cites.
Du, W., Côté, D., Liu, Y.: Saits: Self-attention-based imputation for time series. Expert Systems with Applications 219
2023
Later among the works it cites.
Liu, M., Li, S., Yuan, H., Ong, M.E.H., Ning, Y., Xie, F., Saffari, S.E., Shang, Y., Volovici, V., Chakraborty, B., Liu, N.: Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. Artificial Intelligence in Medicine 142
2023
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2024
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Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., Long, M.: Timesnet: Temporal 2d-variation modeling for general time series analysis. In: The eleventh international conference on learning representations (2022)
2022
Cited alongside, same era.
Yıldız, A.Y., Koç, E., Koç, A.: Multivariate time series imputation with transformers. IEEE Signal Processing Letters 29
2022
Cited alongside, same era.
2024
Closest in time.