The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks
Open accessAbstract
Missing data is a ubiquitous problem. It is especially challenging in medical settings because many streams of measurements are collected at different-and often irregular-times. Accurate estimation of the missing measurements is critical for many reasons, including diagnosis, prognosis, and treatment. Existing methods address this estimation problem by interpolating within data streams or imputing across data streams (both of which ignore important information) or ignoring the temporal aspect of the data and imposing strong assumptions about the nature of the data-generating process and/or the pattern of missing data (both of which are especially problematic for medical data). We propose a new approach, based on a novel deep learning architecture that we call a Multi-directional Recurrent Neural Network that interpolates within data streams and imputes across data streams. We demonstrate the power of our approach by applying it to five real-world medical datasets. We show that it provides dramatically improved estimation of missing measurements in comparison to 11 state-of-the-art benchmarks (including Spline and Cubic Interpolations, MICE, MissForest, matrix completion, and several RNN methods); typical improvements in Root Mean Squared Error are between 35%-50%. Additional experiments based on the same five datasets demonstrate that the improvements provided by our method are extremely robust.
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Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
(2014) · Cited by 33,419
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
Multiple imputation using chained equations: Issues and guidance for practice
White, I. R., Royston, P., & Wood, A. M. (2011) · Statistics in Medicine · doi:10.1002/sim.4067 · Cited by 9,792
MIMIC-III, a freely accessible critical care database
Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141
Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M., & Carpenter, J. R. (2009) · BMJ · doi:10.1136/bmj.b2393 · Cited by 7,299
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471
MissForest—Non-parametric missing value imputation for mixed-type data
Stekhoven, D. J., & Bühlmann, P. (2012) · Bioinformatics · doi:10.1093/bioinformatics/btr597 · Cited by 6,156
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 5,839
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Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era
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Metadata
- Authors
- Yoon, J., Zame, W. R., & van der Schaar, M.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2019-05
- Volume
- 66
- Issue
- 5
- Pages
- 1477-1490
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2770565645
- Cited by
- 341
- References
- 31
- FWCI
- 9.62
- Type
- journal article
- Language
- en
- Open access
- green
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