Abstract
Venous thromboembolism (VTE) poses a significant risk to patients undergoing cancer treatment, particularly in the context of advanced and metastatic disease. In the realm of neuro-oncology, the incidence of VTE varies depending on tumor location and stage, with certain primary and secondary brain tumors exhibiting a higher propensity for thrombotic events. In this study, we employ advanced machine learning techniques, specifically XGBoost, to develop identifying models for predictors searching associated with VTE risk in patients with gliomas. By comparing the diagnosis testing accuracy of our XGBoost models with traditional logistic regression approaches, we aim to enhance our understanding of VTE prediction in this population. Our findings contribute to the growing body of literature on thrombosis risk assessment in cancer patients and may inform the development of personalized prevention and treatment strategies to mitigate the burden of VTE in individuals with gliomas at the hospital term.
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This work was supported by Russian Science Foundation, Grant № 23-11-00346.
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Leontev, S. et al. (2024). Development of a VTE Prediction Model Based on Automatically Selected Features in Glioma Patients. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. ICCS 2024. Lecture Notes in Computer Science, vol 14835. Springer, Cham. https://doi.org/10.1007/978-3-031-63772-8_34
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