guolianmei, sunxiaohong, zhangzhe, tianye. Construction of a Prediction Model for Lower Limb Deep Vein Thrombosis in Patients with Hemorrhagic Stroke Based on Machine Learning. 2025. biomedRxiv.202510.00023
Construction of a Prediction Model for Lower Limb Deep Vein Thrombosis in Patients with Hemorrhagic Stroke Based on Machine Learning
DOI: 10.12201/bmr.202510.00023
-
Abstract: Objective: To construct five distinct machine learning-based risk prediction models for deep venous thrombosis (DVT) in lower extremities among hemorrhagic stroke patients, evaluate their comparative performance, and establish optimal evidence for DVT assessment and prevention strategies.Methods: A retrospective analysis was performed utilizing clinical data from 709 patients who experienced hemorrhagic stroke and were admitted to the Neurosurgical Intensive Care Unit (NICU) at Tianjin Medical University General Hospital from February 2022 to February 2024. Five distinct risk prediction models for deep vein thrombosis (DVT) in the lower extremities of hemorrhagic stroke patients were developed through machine learning algorithms, namely Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN). The models accuracy was assessed through metrics including accuracy, precision, recall, F1 score, and the area under the ROC curve (AUC), while the models’ calibration was evaluated using the Hosmer-Lemeshow test.Results: The prediction model developed utilizing the Random Forest algorithm exhibited the most superior predictive performance, achieving an AUC of 0.947, while the calibration test indicated P > 0.05.SHAP was utilized for the interpretable analysis of the model, pinpointing plasma D-dimer concentration, age, muscle strength in the left lower limb, body mass, the use of sedative and analgesic medications, treatment with medical cooling blankets, and GCS as the primary feature factors.Conclusion: The primary factors affecting DVT in the lower extremities of hemorrhagic stroke patients include plasma D-dimer levels, age, muscle strength of the left lower limb, body mass, the administration of sedatives and analgesics, the use of medical cooling blankets, and the GCS score. The prediction model developed using the Random Forest algorithm exhibits the highest performance.
Key words: Machine Learning; Hemorrhagic Stroke; Deep Vein Thrombosis; Prediction ModelSubmit time: 14 October 2025
Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity. -
图表
-
Lu Xin, Xu Feng. Progress in diagnosis and treatment of deep vein thrombosis after trauma. 2024. doi: 10.12201/bmr.202410.00015
wuzetao. Efficacy Analysis of Two Different Endovascular Thrombectomy Approaches for Treating Early-Stage Lower Extremity Deep Vein Thrombosis: A Single-Center Retrospective Study. 2025. doi: 10.12201/bmr.202506.00037
The predictive value ofperipheral blood inflammatory makers for lower extremity deep venous thrombosis after hip fracture surgery in patients. 2024. doi: 10.12201/bmr.202408.00016
Cheng Luping, Wu Siyang, Lu Bo. Analysis of Risk Factors and Prediction of Type 2 Diabetes Mellitus Based on Machine Learning. 2025. doi: 10.12201/bmr.202509.00003
TANG Shishi, ZHOU Yi. Research on Hemorrhagic Fever with Renal Syndrome Incidence Prediction Based on the SARIMA-LSTM Model. 2024. doi: 10.12201/bmr.202407.00046
lanyushan, lijiao. Machine Learning Methods for Confounding Control in Causal Inference. 2022. doi: 10.12201/bmr.202203.00015
KANG Hongyu, XU Xiaowei, ZHENG Si, HAO Jie, YANG Lin, WANG Xuwen, HOU Li, LI Jiao. Exploring an Industry-Education-Research Collaborative Teaching Model for the Course “R Programming and Fundamentals of Machine Learning” under the New Medical Education Initiative. 2025. doi: 10.12201/bmr.202509.00006
WANG Jie, WANG Zhi-cheng, LOU Shuai, DONG Jian-cheng, CAO Xin-zhi. Research on thyroid nodule detection model based on deep learning algorithm Mask R-CNN. 2024. doi: 10.12201/bmr.202411.00085
HONG Suru, CHEN Yushuang, WU Xiayang. Mortality risk assessment and interpretability analysis of preterm infants in the NICU using machine learning models. 2025. doi: 10.12201/bmr.202503.00066
Construction and application evaluation of risk prediction model and nomogram for shivering during cesarean sectio. 2025. doi: 10.12201/bmr.202501.00053
-
ID Submit time Number Download 1 2025-08-27 bmr.202510.00023V1
Download -
-
Public Anonymous To author only
Get Citation
Article Metrics
- Read: 17
- Download: 0
- Comment: 0