基于DeepFM和XGBoost融合模型的静脉血栓预测

Prediction of Venous Thrombosis Based on Fusion Model of DeepFM and XGBoost

外周穿刺置入中心静脉导管(PICC)技术被广泛运用于中长期静脉治疗.在PICC置管时会导致各种并发症和不良反应,如PICC相关性血栓.随着机器学习和深度神经网络的不断发展与完善,为PICC相关性血栓的辅助诊断提供了基于临床医学数据的解决方法.本文构建了基于DeepFM和XGBoost的融合模型,针对稀疏数据进行特征融合并能降低过拟合的情况,能够对PICC相关性血栓提供风险预测.实验结果表明,融合模型能够有效地对PICC相关性血栓进行特征重要性提取并预测患病概率,辅助临床在外周穿刺置过程中识别血栓高危风险因素,及时进行干预从而预防血栓的发生.

The peripherally inserted central catheter(PICC) technology is widely used in medium and long-term venous treatment, but it can cause various complications and adverse reactions, such as PICC-related thrombosis. The continuous development of machine learning and deep neural networks provides a solution for the assisted diagnosis of PICC-related thrombosis based on clinical medical data. In this study, a fusion model of DeepFM and XGBoost is constructed to predict the risks of PICC-related thrombosis, which can perform feature fusion for sparse data and reduce over-fitting. The experiment reveals that the fusion model can effectively extract the feature importance of PICC-related thrombosis,predict the probability of disease, assist the clinic in identifying high-risk factors of thrombosis in PICC, and intervene to prevent the occurrence of thrombosis in time.