MODELING THE RISK OF PREECLAMPSIA DEVELOPMENT USING ARTIFICIAL INTELLIGENCE METHODS.

Аннотация

Preeclampsia is a multifactorial obstetric complication that occurs in the second half of pregnancy and is characterized by hypertension and dysfunction of various organs, significantly increasing the risk of maternal and perinatal morbidity and mortality. Despite considerable progress in understanding the pathophysiological mechanisms, effective methods for early detection and prediction of preeclampsia remain a pressing issue in clinical practice. This article presents an overview of modern approaches to predicting preeclampsia using artificial intelligence (AI) technologies. Machine learning algorithms, including random forests, gradient boosting, and deep neural networks, are discussed in the context of analyzing clinical, biochemical, and ultrasound data of pregnant women. Particular attention is given to the integration of multimodal data to improve prediction accuracy.