基于DeepFM和XGBoost融合模型的静脉血栓预测

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.

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

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.