Standard
Explainable “white-box” machine learning is the way forward in preeclampsia screening. / Christiansen, Michael; Wilstrup, Casper; Hedley, Paula L.
In:
American Journal of Obstetrics and Gynecology, Vol. 227, No. 5, 2022, p. 791.
Research output: Contribution to journal › Letter › Research
Harvard
Christiansen, M, Wilstrup, C & Hedley, PL 2022, '
Explainable “white-box” machine learning is the way forward in preeclampsia screening',
American Journal of Obstetrics and Gynecology, vol. 227, no. 5, pp. 791.
https://doi.org/10.1016/j.ajog.2022.06.057
APA
Christiansen, M., Wilstrup, C., & Hedley, P. L. (2022).
Explainable “white-box” machine learning is the way forward in preeclampsia screening.
American Journal of Obstetrics and Gynecology,
227(5), 791.
https://doi.org/10.1016/j.ajog.2022.06.057
Vancouver
Christiansen M, Wilstrup C, Hedley PL.
Explainable “white-box” machine learning is the way forward in preeclampsia screening.
American Journal of Obstetrics and Gynecology. 2022;227(5):791.
https://doi.org/10.1016/j.ajog.2022.06.057
Author
Christiansen, Michael ; Wilstrup, Casper ; Hedley, Paula L. / Explainable “white-box” machine learning is the way forward in preeclampsia screening. In: American Journal of Obstetrics and Gynecology. 2022 ; Vol. 227, No. 5. pp. 791.
Bibtex
@article{a89602f112a64851b4724e47b091936b,
title = "Explainable “white-box” machine learning is the way forward in preeclampsia screening",
author = "Michael Christiansen and Casper Wilstrup and Hedley, {Paula L.}",
year = "2022",
doi = "10.1016/j.ajog.2022.06.057",
language = "English",
volume = "227",
pages = "791",
journal = "American Journal of Obstetrics & Gynecology",
issn = "0002-9378",
publisher = "Mosby Inc.",
number = "5",
}
RIS
TY - JOUR
T1 - Explainable “white-box” machine learning is the way forward in preeclampsia screening
AU - Christiansen, Michael
AU - Wilstrup, Casper
AU - Hedley, Paula L.
PY - 2022
Y1 - 2022
UR - http://www.scopus.com/inward/record.url?scp=85135894259&partnerID=8YFLogxK
U2 - 10.1016/j.ajog.2022.06.057
DO - 10.1016/j.ajog.2022.06.057
M3 - Letter
C2 - 35779588
AN - SCOPUS:85135894259
VL - 227
SP - 791
JO - American Journal of Obstetrics & Gynecology
JF - American Journal of Obstetrics & Gynecology
SN - 0002-9378
IS - 5
ER -
