Weighted Heterogeneous Graph-Based Incremental Automatic Disease Diagnosis Method

The objective of this study is to construct a multi-department symptom-based automatic diagnosis model.However,it is dificult to establish a model to classify plenty of diseases and collect thousands of disease-symptom datasets simultaneously.Inspired by the thought of"knowledge graph is model",this study proposes to build an experience-infused knowledge model by continuously learning the experiential knowledge from data,and incrementally injecting it into the knowledge graph.Therefore,incremental learning and injection are used to solve the data collection problem,and the knowledge graph is modeled and containerized to solve the large-scale multi-classification problems.First,an entity linking method is designed and a heterogeneous knowledge graph is constructed by graph fusion.Then,an adaptive neural network model is constructed for each dataset,and the data is used for statistical initialization and model training.Finally,the weights and biases of the learned neural network model are updated to the knowledge graph.It is worth noting that for the incremental process,we consider both the data and class increments.We evaluate the diagnostic effectiveness of the model on the current dataset and the anti-forgetting ability on the historical dataset after class increment on three public datasets.Compared with the classical model,the proposed model improves the diagnostic accuracy of the three datasets by 5%,2%,and 15%on average,respectively.Meanwhile,the model under incremental learning has a better ability to resist forgetting.

基于极限梯度提升和深度神经网络共同决策的心音分类方法

心音是诊断心血管疾病常用的医学信号之一。本文对心音正常/异常的二分类问题进行了研究,提出了一种基于极限梯度提升(XGBoost)和深度神经网络共同决策的心音分类算法,实现了对特征的选择和模型准确率的进一步提升。首先,本文对预处理后的心音信号进行心音分割,在此基础上提取了5个大类的特征,前4类特征采用递归特征消除法进行特征选择,作为XGBoost分类器的输入,最后一类为梅尔频率倒谱系数(MFCC),作为长短时记忆网络(LSTM)的输入。考虑到数据集的不平衡性,本文在两种分类器中皆使用了加权改进的方法。最后采用异质集成决策方法得到预测结果。将本文所提心音分类算法应用于PhysioNet网站在2016年发起的PhysioNet心脏病学挑战赛(CINC)所用公开心音数据库,以测试灵敏度、特异性、修正后的准确率以及F得分,结果分别为93%、89.4%、91.2%、91.3%,通过与其他研究者应用机器学习、卷积神经网络(CNN)等方法的结果比较,在准确率和灵敏度上有明显提高,证明了本文方法能有效地提高心音信号分类的准确性,在部分心血管疾病的临床辅助诊断应用中有很大的潜力。

实现共同富裕的创新手段:碳转移支付

碳达峰、碳中和与共同富裕都是党中央的重要决策。文章以共同富裕中的区域协调发展为主线,以收入与碳排放权的均衡合理分配为原则,构建区域碳转移支付的均衡模型。利用碳转移支付调整区域财政收入,使收入分配体系更加符合科学的共同富裕正态分布特征,从而在兼顾公平和效率、实现共同富裕目标的前提下,达到碳排放权和发展权的平衡,为区域可持续发展奠定基础。