阅读说明：本技术 跨医疗数据源的网络表示学习算法 (Network representation learning algorithm across medical data sources ) 是由 王朝坤 严本成 楼昀恺 石耕源 陈俊 黄海峰 陆超 于 2020-04-03 设计创作，主要内容包括：一种跨医疗数据源的网络表示学习算法,包括：S1,生成包括源网络和目标网络的医疗网络数据；S2,从源网络和目标网络随机采样设定数量的节点；S3,得到一个L层的神经网络,并对每一层分别计算源网络和目标网络的结构特征和表达特征,计算源网络和目标网络的网络特征之间的距离损失；S4,得到源网络在L层神经网络的输出,并根据分类损失和距离损失计算损失值,根据反向传播算法更新算法的参数；S5,重复步骤S2-S4,直至整个算法收敛,使得算法对于疾病分类的准确率在多个迭代内不再上升。有益效果：考虑了不同医院数据源之间数据分布不一致的问题,通过提取网络的结构信息及节点属性信息、最小化特征距离弥补信息损失,有着广阔的应用空间。(A network representation learning algorithm across medical data sources, comprising: s1, generating medical network data comprising a source network and a target network; s2, randomly sampling a set number of nodes from the source network and the target network; s3, obtaining a neural network of L layers, respectively calculating the structural characteristics and expression characteristics of the source network and the target network for each layer, and calculating the distance loss between the network characteristics of the source network and the target network; s4, obtaining the output of the source network in the L-layer neural network, calculating a loss value according to the classification loss and the distance loss, and updating the parameters of the algorithm according to the back propagation algorithm; s5, repeating the steps S2-S4 until the whole algorithm converges, so that the accuracy of the algorithm for disease classification does not rise any more in a plurality of iterations. Has the advantages that: the problem of inconsistent data distribution among different hospital data sources is considered, information loss is made up by extracting the structure information and node attribute information of the network and minimizing the characteristic distance, and the method has a wide application space.)