Building and validating 5-feature models to predict preeclampsia onset time from electronic health record data
- DOI
- 10.1101/2023.03.23.23287655
- Published
- 2023-03-24
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- Not recorded
- Publisher
- openRxiv
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1101/2023.03.23.23287655,
title = {Building and validating 5-feature models to predict preeclampsia onset time from electronic health record data},
author = {Hailey K Ballard and Xiaotong Yang and Aditya Mahadevan and Dominick J Lemas and Lana X Garmire},
year = {2023},
doi = {10.1101/2023.03.23.23287655},
url = {https://doi.org/10.1101/2023.03.23.23287655}
}RIS
TY - JOUR TI - Building and validating 5-feature models to predict preeclampsia onset time from electronic health record data AU - Hailey K Ballard AU - Xiaotong Yang AU - Aditya Mahadevan AU - Dominick J Lemas AU - Lana X Garmire PY - 2023 DO - 10.1101/2023.03.23.23287655 UR - https://doi.org/10.1101/2023.03.23.23287655 ER -
APA
Ballard, H. K., Yang, X., Mahadevan, A., Lemas, D. J., & Garmire, L. X. (2023). Building and validating 5-feature models to predict preeclampsia onset time from electronic health record data. https://doi.org/10.1101/2023.03.23.23287655
Source records
- crossref · retrieved 2026-11-04T18:08:06.533Z