Research Progress on Clinical Prediction Models for Pregnancy Outcomes in Assisted Reproductive Technology
WANG Cong
GONG Zheng
MA Sai-hua
HU Kai-yuan
LANG Meng-ran
XIA Tian
Abstract：The pregnancy outcome of assisted reproductive technology(ART)is influenced by a variety of factors,so the traditional assessment method based on clinical experience may be subjective and inaccurate.Clinical prediction models(CPM)can improve the accuracy and safety of treatment by comprehensively analyzing multimodal variable factors,contributing to precision medicine.Currently,a variety of pregnancy outcome CPMs have been constructed based on diverse algorithms,including not only traditional Logistic regression but also novel nonlinear machine learning algorithms such as random forests,neural networks and deep learning algorithms.Recent advancements in clinical research indicated that the CPMs constructed using a variety of algorithms have the high accuracy and practical potential in predicting pregnancy outcomes of ART.In future,the more accurate and reliable CPMs will be developed by collecting more diverse and representative clinical data,optimizing the model algorithms,engaging in multi-center collaborations and enhancing the generalization capabilities of the CPMs.
Keywords：Reproductive techniquesassistedPregnancy outcomeLogistic regressionMachine learningClinical prediction model
Publication Date：2025-01-14
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 30-35 )