The aim of this study is to develop a machine learning model to accurately predict the risk of venous thromboembolism in patients with cervical cancer after surgery.
Venous thromboembolism (VTE) is a common and life-threatening complication in patients with cervical cancer following surgery. The objective of this study is to develop a machine learning model with the potential to predict the risk of VTE in these patients postoperatively. We plan to employ partial dependence (PD) curves, breakdown (BD) curves, Ceteris-paribus (CP), and SHapley additive exPlanations (SHAP) values for a comprehensive analysis. The goal is to explore how different machine learning algorithms can be utilized as tools for personalized postoperative VTE risk assessment in cervical cancer patients.
Study Type
OBSERVATIONAL
Enrollment
1,174
Chongqing University Cancer Hospital
Chongqing, Chongqing Municipality, China
Whether the patient has developed VTE is determined based on the diagnostic criteria in the "Guidelines for the Prevention and Treatment of Tumor-Associated Venous Thromboembolism (2019 Edition)."
The diagnosis of VTE primarily includes the diagnosis of DVT and PE. According to the guidelines, DVT is diagnosed using venous compression ultrasound or venography, while PE is diagnosed using CT pulmonary angiography (CTPA) or nuclear lung ventilation/perfusion imaging.
Time frame: December 31, 2023
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