NCT06556953

Evaluating the Risk of Postoperative Venous Thromboembolism in Cervical Cancer Patients

Development and Validation of Machine Learning Models to Evaluate the Postoperative Venous Thromboembolism Risk of Cervical Cancer Patients in China

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 1,174 (actual)
• Sponsor: Haike Lei · Academic / Other
• Sex: Female
• Age: 20 Years – 89 Years
• Healthy volunteers: Not accepted

Summary

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.

Detailed description

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.

Conditions

• Venous Thromboembolism

Timeline

• Start date: 2019-01-01
• Primary completion: 2023-12-31
• Completion: 2023-12-31
• First posted: 2024-08-16
• Last updated: 2024-08-16

Locations

1 site across 1 country: China