Machine learning, advanced data analysis, and a role in pregnancy care? How can we help improve preeclampsia outcomes?

被引：2

作者：

Hennessy, Annemarie ^{[1
,2
,3
]}

Tran, Tu Hao ^{[1
,4
]}

Sasikumar, Suraj Narayanan ^{[4
]}

Al-Falahi, Zaidon ^{[3
,4
]}

机构：

[1] Campbelltown Hosp, South Western Sydney Local Hlth Dist, Sydney, Australia

[2] Western Sydney Univ, Sydney, Australia

[3] Univ Sydney, Sydney, Australia

[4] SWERI South Western Emergency Res Inst, Ingham Inst Appl Med Res, Liverpool, Australia

来源：

PREGNANCY HYPERTENSION-AN INTERNATIONAL JOURNAL OF WOMENS CARDIOVASCULAR HEALTH | 2024年 / 37卷

关键词：

Preeclampsia; Hypertension; Risk; Machine learning; Artificial intelligence; HYPERTENSION; PREDICTION;

D O I：

10.1016/j.preghy.2024.101137

中图分类号：

R71 [妇产科学];

学科分类号：

100211 ;

摘要：

The value of machine learning capacity in maternal health, and in particular prediction of preeclampsia will only be realised when there are high quality clinical data provided, representative populations included, different health systems and models of care compared, and a culture of rapid use and application of real-time data and outcomes. This review has been undertaken to provide an overview of the language, and early results of machine learning in a pregnancy and preeclampsia context. Clinicians of all backgrounds are encouraged to learn the language of Machine Learning (ML) and Artificial intelligence (AI) to better understand their potential and utility to improve outcomes for women and their families. This review will outline some definitions and features of ML that will benefit clinician's knowledge in the preeclampsia discipline, and also outline some of the future possibilities for preeclampsia-focussed clinicians via understanding AI. It will further explore the criticality of defining the risk, and outcome being determined.

引用

页数：11

共 39 条

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