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# Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
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The paper can be found here: [https://arxiv.org/abs/1908.00690](https://arxiv.org/abs/1908.00690)
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If you use this repository please cite:
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```
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@ARTICLE{2019arXiv190800690N,
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author = {Bret Nestor, Matthew B.~A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi},
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title = "{Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks}",
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journal = {arXiv e-prints},
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keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
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year = "2019",
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month = "Aug",
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eid = {arXiv:1908.00690},
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pages = {arXiv:1908.00690},
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archivePrefix = {arXiv},
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eprint = {1908.00690},
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primaryClass = {cs.LG},
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adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190800690N},
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adsnote = {Provided by the SAO/NASA Astrophysics Data System}
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}
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```
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