Predicting intensive care need in women with preeclampsia using machine learning – a pilot study-Reference-Cited by-同舟云学术

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Predicting intensive care need in women with preeclampsia using machine learning – a pilot study

Published:2024-02-22 Issue:1 Volume:43 Page:
ISSN:1064-1955
Container-title:Hypertension in Pregnancy
language:en
Short-container-title:Hypertension in Pregnancy

Author:

Edvinsson Camilla¹²³,Björnsson Ola⁴⁵,Erlandsson Lena¹,Hansson Stefan R.¹⁶^ORCID

Affiliation:

1. Division of Obstetrics and Gynecology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden

2. Division of Anaesthesia and Intensive Care, Department of Clinical Sciences Lund, Lund University, Lund, Sweden

3. Department of Anaesthesia and Intensive Care, Helsingborg Hospital, Helsingborg, Sweden

4. Division of Mathematical Statistics, Centre for Mathematical Sciences, Lund University, Lund, Sweden

5. Department of Energy Sciences, Faculty of Engineering, Lund University, Lund, Sweden

6. Department of Obstetrics and Gynecology, Skåne University Hospital, Lund/Malmö, Sweden

Funder

authors have read and agreed to the published

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10641955.2024.2312165

Reference46 articles.

1. Preeclampsia

2. Syncytiotrophoblast stress in preeclampsia: the convergence point for multiple pathways

3. First trimester preeclampsia screening and prediction

4. Aspirin versus Placebo in Pregnancies at High Risk for Preterm Preeclampsia

5. Eclampsia in the 21st century

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning, advanced data analysis, and a role in pregnancy care? How can we help improve preeclampsia outcomes?;Pregnancy Hypertension;2024-09

2. Delayed versus early delivery leads to similar outcome in selected cases of preeclampsia in the Finnish Genetics of Pre-eclampsia Consortium (FINNPEC) cohort;Pregnancy Hypertension;2024-06

3. Use of Machine Learning for Early Detection of Maternal Cardiovascular Conditions: Retrospective Study Using Electronic Health Record Data;JMIR Cardio;2024-04-22

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