Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data

Authors

Jiarui Feng, Washington University School of Medicine in St. Louis
Jennifer Lee, Washington University School of Medicine in St. Louis
Zachary A Vesoulis, Washington University School of Medicine in St. Louis
Fuhai Li, Washington University School of Medicine in St. Louis

Journal

npj Digital Medicine

Publication Date

7-14-2021

Volume

4

Issue

1

First Page

108

Document Type

Open Access Publication

DOI

10.1038/s41746-021-00479-4

Rights and Permissions

Feng, J., Lee, J., Vesoulis, Z.A. et al. Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data. npj Digit. Med. 4, 108 (2021). doi.org/10.1038/s41746-021-00479-4 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Recommended Citation

Feng, Jiarui; Lee, Jennifer; Vesoulis, Zachary A; and Li, Fuhai, "Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data." npj Digital Medicine. 4, 1. 108 (2021).
https://digitalcommons.wustl.edu/open_access_pubs/11681