Five-Feature Models to Predict Preeclampsia Onset Time From Electronic Health Record Data: Development and Validation Study
- DOI
- 10.2196/48997
- Published
- 2024-08-14
- Container
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.2196/48997,
title = {Five-Feature Models to Predict Preeclampsia Onset Time From Electronic Health Record Data: Development and Validation Study},
author = {Hailey K Ballard and Xiaotong Yang and Aditya D Mahadevan and Dominick J Lemas and Lana X Garmire},
year = {2024},
journal = {Journal of Medical Internet Research},
doi = {10.2196/48997},
url = {https://doi.org/10.2196/48997}
}RIS
TY - JOUR TI - Five-Feature Models to Predict Preeclampsia Onset Time From Electronic Health Record Data: Development and Validation Study AU - Hailey K Ballard AU - Xiaotong Yang AU - Aditya D Mahadevan AU - Dominick J Lemas AU - Lana X Garmire PY - 2024 JO - Journal of Medical Internet Research DO - 10.2196/48997 UR - https://doi.org/10.2196/48997 ER -
APA
Ballard, H. K., Yang, X., Mahadevan, A. D., Lemas, D. J., & Garmire, L. X. (2024). Five-Feature Models to Predict Preeclampsia Onset Time From Electronic Health Record Data: Development and Validation Study. Journal of Medical Internet Research. https://doi.org/10.2196/48997
Source records
- crossref · retrieved 2026-09-24T20:26:00.793Z