Perinatal health predictors using artificial intelligence: A review

Abstract

Advances in public health and medical care have enabled better pregnancy and birth outcomes. The rates of perinatal health indicators such as maternal mortality and morbidity; fetal, neonatal, and infant mortality; low birthweight; and preterm birth have reduced over time. However, they are still a public health concern, and considerable disparities exist within and between countries. For perinatal researchers who are engaged in unraveling the tangled web of causation for maternal and child health outcomes and for clinicians involved in the care of pregnant women and infants, artificial intelligence offers novel approaches to prediction modeling, diagnosis, early detection, and monitoring in perinatal health. Machine learning, a commonly used artificial intelligence method, has been used to predict preterm birth, birthweight, preeclampsia, mortality, hypertensive disorders, and postpartum depression. Real-time electronic health recording and predictive modeling using artificial intelligence have found early success in fetal monitoring and monitoring of women with gestational diabetes especially in low-resource settings. Artificial intelligence–based methodologies have the potential to improve prenatal diagnosis of birth defects and outcomes in assisted reproductive technology too. In this scenario, we envision artificial intelligence for perinatal research to be based on three goals: (1) availability of population-representative, routine clinical data (rich multimodal data of large sample size) for perinatal research; (2) modification and application of current state-of-the-art artificial intelligence for prediction and classification in health care research to the field of perinatal health; and (3) development of methods for explaining the decision-making processes of artificial intelligence models for perinatal health indicators. Achieving these three goals via a multidisciplinary approach to the development of artificial intelligence tools will enable trust in these tools and advance research, clinical practice, and policies to ensure optimal perinatal health.

Application of machine learning methods for predicting infant mortality in Rwanda: analysis of Rwanda demographic health survey 2014–15 dataset

“…Although this infant mortality remains a public health burden particularly in developing countries, there is a significant progress in child health due to efforts and strategies taken for combating risk factors of this health burden [ 4 ]. Recent studies indicated that an automatic intelligent system based on artificial intelligence (AI), machine learning (ML) and deep learning (DL) have the potential to accelerate this progress [ 5 ] and these methods have demonstrated the significant performance in various fields including public health and medicine domains [ 4 , 6 – 8 ]. These methods are followed by computational intelligence in this progress [ 9 ].…”

“…Moreover, previous studies that utilised ML approaches to predict infant mortality reported that Random Forest performed well with an accuracy that varies from 67.2% to 97.1%, followed by Logistic Regression model with 86.1%, and K-nearest neighbour with 85.6% [ 25 , 26 ]. In addition to that, the ML models have potentials to perform better than the traditional statistical models because their ability to deal with non-linear complex data, multiple interactions between determinants and handle multiple factors and chain of events simultaneously [ 5 , 27 ]. Also, ML is a prediction method that importantly determine not only who are a high risk to be died but also when the women and infants are at a higher risk [ 13 , 28 ].…”

“…Also, ML is a prediction method that importantly determine not only who are a high risk to be died but also when the women and infants are at a higher risk [ 13 , 28 ]. Comparing traditional models, ML methods can disentangle relations that are nonlinear or binary and can produce highly stable estimates or predictions in large datasets that are frequently challenging to find correlations [ 5 , 27 , 29 ].…”

Reproductive decision-making of Black women living with HIV: A systematic review

“…Evidence of disparities between health systems in higher- and lower-income countries is represented by the differences in maternal and infant mortality and morbidity rates. 82 …”

Methodological Considerations for Preterm Birth Research

“…This is particularly important given the increased use of novel artificial intelligence techniques like machine learning (ML). The best ML models use high quality real time and existing data for predictive modeling and early diagnosis of health outcomes such as PTB ( 7 ). Unlike traditional statistical models, ML can handle more complex data structures.…”