Early prediction of preeclampsia via machine learning

Contribution of Artificial Intelligence in Pregnancy: A Scoping Review

“…The majority of the studies (n=22) were published between 2016 and 2020. AI techniques were used for predicting pregnancy disorders/complications in about 75% (n=18) of the included studies [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Specifically, the techniques discussed were utilized for predicting preeclampsia [5,7,9,15], preterm birth [6,13,19], gestational diabetes [8,14,21], gestational age [4,18], patient's metabolomics profile [12,20], suicidal behavior [11], uterine contractions [16], labor due date [17], and hypertensive disorder [10].…”

Risk factors for postpartum readmission for preeclampsia or hypertension before delivery discharge among low-risk women: a case-control study

“…Notably, 2 models have been recently published using machine learning methods to develop predictive models for preeclampsia during pregnancy and postpartum readmission for preeclampsia, respectively, using data from the electronic health record. 12,23 Using the current study, it may be possible that these risk factors could be integrated into a clinical predictive model. If targeted to patients at the highest risk of readmission, these interventions could reduce postpartum readmission rates and improve maternal health, which will have to be studied.…”

Data-Driven Modeling of Pregnancy-Related Complications

“…Administrative insurance claims data, containing large sample sizes over large geographic areas but with less granularity, have been successfully mined to create and analyze pregnancy cohorts [35][36][37]. Studies involving EHR-based models for the prediction of gestational diabetes mellitus (GDM), preeclampsia, and PTB illustrate how such data sources are relevant in the context of reproductive health [38][39][40]. EHRs can also be used to characterize the currently unknown pharmacological effects that a broad range of drugs might have on the physiology of pregnancy [41,42].…”

“…Machine learning is well suited for predictive modeling of pregnancy outcomes [91,92] and is becoming more prevalent [93][94][95][96][97][98][99][100][101][102][103][104][105] due to its ability to model highly complex relationships between measured features and outcomes. The majority of previous work focused on modeling techniques that incorporate one or two data sources, including clinical [95] as well as derived numerical data from another source such as blood samples [39], Doppler ultrasound, echosonography, or magnetic resonance imaging (MRI) readings [101], or mental health assessments [106]. Together, these datasets are combined into a single, structured table of samples and features.…”