Publications by authors named "Yunlu Ping"
Background: Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate.
Objective: This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer.
Article Synopsis
- The study developed and validated a clinical prediction model to assess the risk of pelvic inflammatory disease (PID) progressing to sepsis using both random survival forest (RSF) and stepwise Cox regression methods.
- A retrospective cohort study analyzed clinical data from PID patients diagnosed between 2008 and 2019, identifying key predictive factors such as dialysis, platelet counts, and history of pneumonia.
- The nomogram and RSF models showed high predictive performance, with the RSF demonstrating superior accuracy compared to the Cox regression models in estimating sepsis risk within 3 and 7 days.
To evaluate the association between gene polymorphisms of MTHFR (C677T, A1298C) and MTRR (A66G), and the recurrent spontaneous abortion (RSA) risk in Asia.Related case-control studies were collected, selected, and screened. A meta-analysis was conducted by Stata 12.
View Article and Find Full Text PDFTo evaluate the associations between Tumor necrosis factor-α (TNF-α)(-238G>A) and Interleukin-6 (IL-6)(-174G>C) polymorphism and risk of unexplained recurrent spontaneous abortion (URSA).Correlated case-control studies were collected by computer retrieval. A meta-analysis was conducted by Stata 12.
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