Pelvic Floor Dysfunction (PFD) is an umbrella term for a range of disorders affecting the function of the pelvic floor muscles and tissues in women. It not only impacts the quality of life for women but can also lead to psychosocial issues. With the advancement of medical big data and artificial intelligence technologies, the study of disease prediction models has become a hot topic in the medical field. These models can assist physicians in early screening during pregnancy and the postpartum period to identify high-risk groups for disease development, allowing for proactive preventive measures and a reduction in disease incidence. This review aims to provide a comprehensive framework for identifying and discussing the strengths and limitations of existing models, as well as exploring potential directions for their development. The prediction models encompass a variety of types, from statistical methods to machine learning, including but not limited to nomograms, logistic regression, decision trees, support vector machines, and neural networks. We hope to provide valuable insights for clinicians, researchers, and policymakers, ultimately improving the prognosis and quality of life for postpartum patients.

Lulu Chen, Xiaoyan Jia, Hua Zhang. Advances in the Study of Risk Prediction Models for Postpartum Pelvic Floor Dysfunction Diseases. Academic Journal of Medicine & Health Sciences (2024), Vol. 5, Issue 8: 1-6. https://doi.org/10.25236/AJMHS.2024.050801.

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