Pregnancy is a critical period for women, and they require special care and attention to ensure that both the mother and the fetus are healthy. During pregnancy, women experience various physiological changes that can affect their overall health. In addition, several complications can arise during pregnancy, such as gestational diabetes, hypertension, pre-eclampsia, and preterm labor. Early detection and management of these complications are essential to ensure that both the mother and the baby are safe. The use of machine learning-based devices during pregnancy has the potential to improve maternal and fetal health outcomes. This chapter provides an overview of machine learning-based devices for pregnant women.
Top2. Machine Learning And Pregnancy
Machine learning has been applied to various aspects of healthcare, including pregnancy. In this chapter, we will discuss how machine learning has been used in pregnancy-related applications such as predicting preterm birth, detecting preeclampsia, and improving neonatal care.
Predicting Preterm Birth
Preterm birth, defined as birth before 37 weeks of gestation, is a significant public health concern worldwide. Machine learning algorithms have been used to predict preterm birth based on various factors, such as maternal age, race, medical history, and cervical length.
Several studies have shown promising results in using machine learning algorithms to predict preterm birth. For example, a study conducted by Gal et al. (2020) developed a machine learning model that achieved an area under the curve (AUC) of 0.84 in predicting preterm birth. Another study by Kindinger et al. (2020) developed a machine learning model that achieved an AUC of 0.73 in predicting preterm birth.
Detecting Preeclampsia
Preeclampsia is a serious pregnancy-related condition characterized by high blood pressure and damage to organs such as the liver and kidneys. Early detection and treatment of preeclampsia can improve maternal and fetal outcomes. Machine learning algorithms have been used to detect preeclampsia based on various factors, such as maternal age, blood pressure, and proteinuria.
Several studies have shown promising results in using machine learning algorithms to detect preeclampsia. For example, a study conducted by Fenton et al. (2019) developed a machine learning model that achieved an AUC of 0.86 in detecting preeclampsia. Another study by Mahmoodian et al. (2021) developed a machine learning model that achieved an AUC of 0.89 in detecting preeclampsia.
Improving Neonatal Care
Neonatal care is critical for the health and well-being of newborns. Machine learning algorithms have been used to improve neonatal care by predicting outcomes such as neonatal mortality, length of hospital stay, and risk of neonatal sepsis.
Several studies have shown promising results in using machine learning algorithms to improve neonatal care. For example, a study conducted by Chock et al. (2019) developed a machine learning model that achieved an AUC of 0.88 in predicting neonatal mortality. Another study by Zadrozny et al. (2019) developed a machine learning model that achieved an AUC of 0.85 in predicting the risk of neonatal sepsis.
3. Machine Learning-Based Devices For Monitoring Fetal Health
Monitoring fetal health is a crucial aspect of prenatal care, as it helps healthcare providers detect potential problems early and take appropriate actions to ensure the well-being of both the mother and the fetus. In recent years, machine learning-based devices have emerged as powerful tools for monitoring fetal health, as they can analyze large volumes of data and detect patterns that may not be visible to the human eye. In this chapter, we will discuss some of the machine learning-based devices that are currently used for monitoring fetal health.