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Dynamic Ensemble Selection for Early Detection of Deep Vein Thrombosis in Fracture Patients.

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Title: Dynamic Ensemble Selection for Early Detection of Deep Vein Thrombosis in Fracture Patients.
Authors: Li, Jian1,2 (AUTHOR), Cheng, Si-yuan1,2 (AUTHOR), Zhang, Shu-rui1,2 (AUTHOR), Zhou, Shi-dong1,2 (AUTHOR), Jin, Hai-jiang3 (AUTHOR), Du, Qiu-xiang1,2 (AUTHOR), Cao, Jie1,2 (AUTHOR), Jin, Qian-qian1,2 (AUTHOR) qianqian.jin@sxmu.edu.cn, Sun, Jun-hong1,2 (AUTHOR) junhong.sun@sxmu.edu.cn
Source: Journal of Medical Systems. 1/14/2026, Vol. 50 Issue 1, p1-12. 12p.
Subjects: Risk assessment, Ensemble learning, Boosting algorithms, Glucose, Random forest algorithms, Multilayer perceptrons, Prediction models, Receiver operating characteristic curves, Academic medical centers, Cluster analysis (Statistics), Computer software, Research funding, Probability theory, Venous thrombosis, Research evaluation, Body weight, Logistic regression analysis, Statistical sampling, Age distribution, Fibrin fibrinogen degradation products, Retrospective studies, Bone fractures, Longitudinal method, Electronic health records, Medical records, Acquisition of data, Accuracy, Albumins, Decision trees, Confidence intervals, Calibration, Sensitivity & specificity (Statistics), Algorithms, Disease risk factors, Disease complications
Geographic Terms: China
Abstract: Deep vein thrombosis (DVT) in fracture patients is often clinically silent, with a high incidence of thrombosis and associated mortality. Static machine learning methods struggle to address the challenge of early DVT diagnosis due to their inability to adapt to heterogeneous data across patients. In contrast, Dynamic Ensemble Selection (DES) improves clinical decision-making and therapeutic interventions by dynamically adapting to variations in data characteristics. Here, we developed and validated a risk prediction model for DVT using electronic medical record data from fracture patients upon admission. By employing the DES method to optimize the prediction process, the model generates patient-specific probabilities of DVT occurrence, enabling personalized clinical risk assessment. Validation results showed that the DES model achieved strong performance in predicting DVT, with an accuracy of 0.875 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.906. Notably, it demonstrated a high recall of 0.918 for DVT. Furthermore, in the prospective test set, DES exhibited excellent generalization capability, maintaining robust performance with an accuracy of 0.813 and an AUC of 0.876. We further developed an interactive clinical tool based on the DES algorithm to facilitate model interpretation and implementation. By integrating this user-friendly solution into clinical workflows, DES not only improves early DVT detection but also optimizes the allocation of healthcare resources. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Deep vein thrombosis (DVT) in fracture patients is often clinically silent, with a high incidence of thrombosis and associated mortality. Static machine learning methods struggle to address the challenge of early DVT diagnosis due to their inability to adapt to heterogeneous data across patients. In contrast, Dynamic Ensemble Selection (DES) improves clinical decision-making and therapeutic interventions by dynamically adapting to variations in data characteristics. Here, we developed and validated a risk prediction model for DVT using electronic medical record data from fracture patients upon admission. By employing the DES method to optimize the prediction process, the model generates patient-specific probabilities of DVT occurrence, enabling personalized clinical risk assessment. Validation results showed that the DES model achieved strong performance in predicting DVT, with an accuracy of 0.875 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.906. Notably, it demonstrated a high recall of 0.918 for DVT. Furthermore, in the prospective test set, DES exhibited excellent generalization capability, maintaining robust performance with an accuracy of 0.813 and an AUC of 0.876. We further developed an interactive clinical tool based on the DES algorithm to facilitate model interpretation and implementation. By integrating this user-friendly solution into clinical workflows, DES not only improves early DVT detection but also optimizes the allocation of healthcare resources. [ABSTRACT FROM AUTHOR]
ISSN:01485598
DOI:10.1007/s10916-025-02299-5