Machine-learning model for the prediction of preeclampsia – a step toward personalized risk assessment

Research output: Contribution to journal › Meeting Abstract › peer-review

Original language	English
Pages (from-to)	171-171
Number of pages	1
Journal	American Journal of Obstetrics and Gynecology
Volume	226
Issue number	1
DOIs	https://doi.org/10.1016/j.ajog.2021.11.300
State	Published - Feb 2022

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title = "Machine-learning model for the prediction of preeclampsia – a step toward personalized risk assessment",

author = "Guy Shtar and Lior Rokach and Victor Novack and Lena Novack and Than, \{Nandor Gabor\} and Offer Erez and Amnon Hadar",

year = "2022",

month = feb,

doi = "10.1016/j.ajog.2021.11.300",

language = "English",

volume = "226",

pages = "171--171",

journal = "American Journal of Obstetrics and Gynecology",

issn = "0002-9378",

publisher = "Elsevier Inc.",

number = "1",

}

TY - JOUR

T1 - Machine-learning model for the prediction of preeclampsia – a step toward personalized risk assessment

AU - Shtar, Guy

AU - Rokach, Lior

AU - Novack, Victor

AU - Novack, Lena

AU - Than, Nandor Gabor

AU - Erez, Offer

AU - Hadar, Amnon

PY - 2022/2

Y1 - 2022/2

U2 - 10.1016/j.ajog.2021.11.300

DO - 10.1016/j.ajog.2021.11.300

M3 - Meeting Abstract

SN - 0002-9378

VL - 226

SP - 171

EP - 171

JO - American Journal of Obstetrics and Gynecology

JF - American Journal of Obstetrics and Gynecology

IS - 1

ER -