Workflow of TempVTE. (IMAGE)
Caption
A lightweight, interpretable machine learning model for venous thromboembolism (VTE) risk estimation in cervical cancer patients post-treatment. The temporal feature vector captures the dynamic evolution of key biomarkers, providing insights into the mechanisms of VTE and improving model interpretability.
Credit
Can Xu, Xinni Xiang, Yongtao Wu, Haike Lei, Jie Yang
Usage Restrictions
Credit must be given to the creator. Only noncommercial uses of the work are permitted. No derivatives or adaptations of the work are permitted.
License
CC BY-NC-ND