| Abstract |
This literature study paper aims to explore the role of clinical decision support systems
in pregnancy care and their potential for reducing maternal mortality. AI technologies have the
potential to transform prenatal, perinatal, and postnatal care by enhancing the accuracy of
diagnoses, personalizing treatment plans, and predicting complications before they become critical.
The review synthesizes findings from various studies, highlighting the application of machine
learning algorithms in analyzing complex medical data, such as ultrasound images and genomic
data, to support clinical decisions.The paper will focus on the use of artificial intelligence and
machine learning models in CDSSs for various study targets such as birth mode prediction,
pregnancy risk prediction, fetal state monitoring, risk level prediction, childbirth prediction,
treatment prediction, and infection prediction. The study will analyze the effectiveness of these
CDSSs in improving pregnancy outcomes and reducing maternal mortality rates.Various machine
learning and deep learning algorithms have been employed to address different tasks. Additionally,
the paper will also discuss the challenges and limitations of implementing CDSSs in pregnancy
care and provide recommendations for future research in this area. |
