Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage
Abstract Lower extremity deep vein thrombosis is one of the important complications of spontaneous intracerebral hemorrhage. We aimed to develop a risk assessment model to predict the risk of lower extremity DVT during hospitalization in patients with spontaneous cerebral hemorrhage. The retrospecti...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-10905-2 |
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| author | Weizhi Qiu Penglei Cui Shaojie Li Zhenzhou Tang Jiani Chen Jiayin Wang Yasong Li |
| author_facet | Weizhi Qiu Penglei Cui Shaojie Li Zhenzhou Tang Jiani Chen Jiayin Wang Yasong Li |
| author_sort | Weizhi Qiu |
| collection | DOAJ |
| description | Abstract Lower extremity deep vein thrombosis is one of the important complications of spontaneous intracerebral hemorrhage. We aimed to develop a risk assessment model to predict the risk of lower extremity DVT during hospitalization in patients with spontaneous cerebral hemorrhage. The retrospective study began by randomly dividing the data into a training set and a test set in a 7:3 ratio. Feature selection was performed in the training set, and Boruta and LASSO algorithms were used to screen significant predictors. Five machine learning algorithms were used to construct the prediction model and the model accuracy was evaluated by ROC curves. To validate the model, we constructed calibration curves and compared the calibration of the model using the Brier score. Finally, the clinical value of the model was assessed by Decision Clinical Curve (DCA) and the “black box” model was interpreted by SHAP. The training and test sets did not show significant differences between the individual variables. Screening by the LASSO and Boruta algorithms yielded 15 and 7 potentially relevant variables, respectively, resulting in the identification of six significant predictors associated with DVT. Subsequently, the performance of five machine learning algorithms in DVT prediction was evaluated in the test set. These results suggest that the LGBM model has significant advantages in predicting DVT after cerebral hemorrhage. We developed a model to predict the risk of lower extremity deep vein thrombosis during hospitalization in patients with spontaneous cerebral hemorrhage, and this model can accurately identify high-risk patients. |
| format | Article |
| id | doaj-art-91ec013376bc4a04bef9368f18fd92b7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-91ec013376bc4a04bef9368f18fd92b72025-08-20T04:03:06ZengNature PortfolioScientific Reports2045-23222025-07-0115111210.1038/s41598-025-10905-2Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhageWeizhi Qiu0Penglei Cui1Shaojie Li2Zhenzhou Tang3Jiani Chen4Jiayin Wang5Yasong Li6Department of Neurosurgery, The Second Affiliated Hospital of Fujian Medical UniversityDepartment of Neurosurgery, The Second Affiliated Hospital of Fujian Medical UniversityDepartment of Neurosurgery, The Second Affiliated Hospital of Fujian Medical UniversityDepartment of Neurosurgery, The Second Affiliated Hospital of Fujian Medical UniversityDepartment of Neurosurgery, The Second Affiliated Hospital of Fujian Medical UniversityDepartment of Neurosurgery, The Second Affiliated Hospital of Fujian Medical UniversityDepartment of Neurosurgery, The Second Affiliated Hospital of Fujian Medical UniversityAbstract Lower extremity deep vein thrombosis is one of the important complications of spontaneous intracerebral hemorrhage. We aimed to develop a risk assessment model to predict the risk of lower extremity DVT during hospitalization in patients with spontaneous cerebral hemorrhage. The retrospective study began by randomly dividing the data into a training set and a test set in a 7:3 ratio. Feature selection was performed in the training set, and Boruta and LASSO algorithms were used to screen significant predictors. Five machine learning algorithms were used to construct the prediction model and the model accuracy was evaluated by ROC curves. To validate the model, we constructed calibration curves and compared the calibration of the model using the Brier score. Finally, the clinical value of the model was assessed by Decision Clinical Curve (DCA) and the “black box” model was interpreted by SHAP. The training and test sets did not show significant differences between the individual variables. Screening by the LASSO and Boruta algorithms yielded 15 and 7 potentially relevant variables, respectively, resulting in the identification of six significant predictors associated with DVT. Subsequently, the performance of five machine learning algorithms in DVT prediction was evaluated in the test set. These results suggest that the LGBM model has significant advantages in predicting DVT after cerebral hemorrhage. We developed a model to predict the risk of lower extremity deep vein thrombosis during hospitalization in patients with spontaneous cerebral hemorrhage, and this model can accurately identify high-risk patients.https://doi.org/10.1038/s41598-025-10905-2Spontaneous cerebral hemorrhageLower extremity deep vein thrombosisMachine learningRetrospective analysisRisk assessment models |
| spellingShingle | Weizhi Qiu Penglei Cui Shaojie Li Zhenzhou Tang Jiani Chen Jiayin Wang Yasong Li Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage Scientific Reports Spontaneous cerebral hemorrhage Lower extremity deep vein thrombosis Machine learning Retrospective analysis Risk assessment models |
| title | Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage |
| title_full | Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage |
| title_fullStr | Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage |
| title_full_unstemmed | Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage |
| title_short | Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage |
| title_sort | machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage |
| topic | Spontaneous cerebral hemorrhage Lower extremity deep vein thrombosis Machine learning Retrospective analysis Risk assessment models |
| url | https://doi.org/10.1038/s41598-025-10905-2 |
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