基于数字孪生和智能感知的虚拟分流技术研究

Research on virtual shunt technology based on digital twinning and intelligent sensing

针对智能分诊技术难以处理海量数据且存在主观性较强的问题,文中基于数字孪生技术理论和智能感知算法提出了一种虚拟的医疗机构分诊模型。该模型由文本分类算法及图像分类算法组成,其中文本分类算法将Word2Vec与VSM算法相结合,大幅提升了生成文本词向量的效率和质量,同时还具有消歧作用。而图像分类算法采用CNN及RBM算法来进行特征提取和分类,且为了减小算法的依赖性,通过注意力机制输出诊断结果,并利用Softmax层对数据加以融合。实验结果表明,所提算法可以有效、准确地输出诊断结果,相较于对比算法,其文本分类准确率平均提升了约3.5%,图像分类的F1值则平均提升约3%,说明该算法具有良好的性能及应用价值。

In view of the shortcomings of intelligent triage technology,which is difficult to handle massive data and has strong subjectivity,this paper proposes a virtual triage model for medical institutions based on digital twin technology theory and intelligent perception algorithm.The model is composed of text classification algorithm and image classification algorithm.The text classification algorithm combines Word2Vec and VSM algorithm,which greatly improves the efficiency and quality of the generated text word vector,and also has disambiguation effect.The image classification algorithm uses CNN for feature extraction and RBM for feature classification.In order to reduce the dependence of the algorithm,the diagnosis results are output through the attention mechanism,and the data is fused using the Softmax layer.The experimental results show that the proposed algorithm can effectively and accurately output the diagnosis results,the text classification accuracy of the algorithm is about 3.5%higher than that of the comparison algorithm,and the text classification F1 value is about 3%higher,which shows that the algorithm has good performance and engineering application value.