【摘要】 该文目标是构建一个能实现多科室级别的基于症状的疾病自动诊断模型。但构建分类几千种疾病的模型、同时收集成千上万种疾病-症状数据集这两个任务是现有研究难以解决的。基于“知识图谱即是模型”的想法,提出了通过不断学习数据中的经验知识,增量式地注入到知识图谱中,以此来构建一个注入“经验”的知识模型。即通过增量式学习、注入来解决数据收集问题,通过将知识图谱模型化、容器化来解决超多分类问题。首先通过图谱融合构建了一份异构知识图谱并设计了一个实体链接方法。然后对于每份数据集构建一个自适应的神经网络模型,利用数据实现统计学初始化和模型训练。最后将学习完成的神经网络模型中权重和偏置更新到异构图谱中。对于增量过程,同时考虑了数据增量和类别增量两种情况。在三份公共数据集上评估了模型在当前数据集上的诊断效果,以及类别增量后对历史数据集的抗遗忘能力两个性能;与经典模型相比,诊断正确率分别平均提高了5%、2%和15%;同时模型在增量学习下具有较好的抗遗忘能力。更多还原

【Abstract】 The objective of this study is to construct a multi-department symptom-based automatic diagnosis model.However,it is difficult to establish a model to classify plenty of diseases and collect thousands of diseasesymptom 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 by5%,2%,and 15% on average,respectively.Meanwhile,the model under incremental learning has a better ability to resist forgetting.更多还原