医学知识推理研究现状与发展

doi:10.3778/j.issn.1673-9418.2111031

计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (6): 1193-1213.DOI: 10.3778/j.issn.1673-9418.2111031

医学知识推理研究现状与发展

董文波¹, 孙仕亮¹^,⁺(), 殷敏智²

1. 华东师范大学计算机科学与技术学院,上海 200062
2. 上海交通大学医学院附属上海儿童医学中心病理科,上海 200127

收稿日期:2021-11-04 修回日期:2022-01-20 出版日期:2022-06-01 发布日期:2022-06-20
通讯作者: + E-mail: slsun@cs.ecnu.edu.cn
作者简介:董文波（1992—）,男,河南新乡人,博士研究生,主要研究方向为模式识别与机器学习、知识图谱推理等。
孙仕亮（1979—）,男,博士,教授,博士生导师,CCF会员,主要研究方向为多视图学习、模式识别、机器学习。
殷敏智（1970—）,女,浙江宁波人,主任医师,主要研究方向为儿童肿瘤的病理诊断、AI在病理诊断中的应用。
基金资助:
国家自然科学基金(62076096);上海知识服务平台项目(ZF1213)

Research and Development of Medical Knowledge Graph Reasoning

DONG Wenbo¹, SUN Shiliang¹^,⁺(), YIN Minzhi²

1. School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
2. Department of Pathology, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China

Received:2021-11-04 Revised:2022-01-20 Online:2022-06-01 Published:2022-06-20
About author:DONG Wenbo, born in 1992, Ph.D. candidate. His research interests include pattern recognition and machine learning, knowledge graph reasoning, etc.
SUN Shiliang, born in 1979, Ph.D., professor, Ph.D. supervisor, member of CCF. His research interests include multi-view learning, pattern recognition and machine learning.
YIN Minzhi, born in 1970, chief physician. Her research interests include the pathological diagnosis of children’s tumors and the application of AI in pathological diagnosis.
Supported by:
National Natural Science Foundation of China(62076096);Project of Shanghai Knowledge Service Platform(ZF1213)

摘要/Abstract

摘要：

知识图谱可以有效地组织和表示知识,被应用于很多高级应用中,比如智能医疗。然而,无论是人工还是自动化构建的医学知识图谱通常是不完整的,这严重限制了它们的使用性能。医学知识推理可以补全医学知识图谱,并可辅助医生进行医学诊断。首先给出了医学知识推理的基本概念和定义,然后对构建医学知识图谱的关键技术和基于医学知识推理的辅助诊断进行了总结与归纳,并重点回顾了医学知识推理研究现状,将其推理方法划分为基于逻辑规则的医学推理、基于表示学习的医学推理以及基于深度学习的医学推理。对于每一类别,分别介绍了代表性算法和最新研究进展。特点是在现有方法的基础上对基于医学知识图谱的推理技术进行了综合的介绍。最后总结了医学知识推理目前面对的一些挑战和重要问题,并展望了其发展前景和研究趋势,希望能促进这一快速发展领域的进一步研究。

关键词: 知识图谱, 智能医疗, 知识推理, 医学辅助诊断, 医学知识补全

Abstract:

Knowledge graphs can effectively organize and represent knowledge, which have been applied to many advanced applications, for example, intelligent medicine. However, the medical knowledge graphs constructed manually or automatically are usually incomplete, which seriously limits their performance. Medical knowledge reasoning can complete medical knowledge graph and assist doctors in medical diagnosis. This paper first gives the basic concept and definition of medical knowledge reasoning, and then summarizes the key technologies of constructing medical knowledge graphs and the auxiliary diagnosis methods based on medical knowledge reasoning. Subsequently, this paper reviews the research development of medical knowledge reasoning, and classifies its reasoning methods into rule-based medical reasoning, representation learning-based medical reasoning and deep learning-based medical reasoning. For each category, representative algorithms and newly proposed algorithms are presented. The main feature of this survey is that it provides a comprehensive introduction for the recent development of knowledge graph reasoning on the basis of coherence with early methods. Finally, this paper prospects the development of medical knowledge reasoning based on the major challenges and key problems faced by medical knowledge reasoning, hoping to promote further research in this rapidly developing field.

Key words: knowledge graph, intelligent medicine, knowledge reasoning, medical assistant diagnosis, medical knowledge completion

中图分类号:

TP181
TP399

董文波, 孙仕亮, 殷敏智. 医学知识推理研究现状与发展[J]. 计算机科学与探索, 2022, 16(6): 1193-1213.

DONG Wenbo, SUN Shiliang, YIN Minzhi. Research and Development of Medical Knowledge Graph Reasoning[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(6): 1193-1213.

图/表 8

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方法	优点	不足
基于医学词典和规则	根据医学词典和规则抽取,准确率高	依赖专家编写的规则和医学词典,难以适应数据不断变化的现实情况
基于机器学习方法	利用医学数据特点进行模型训练,相对简便,识别效率较高	需要高质量的数据进行训练,因而对人工标注数据的专业性要求较高
基于深度学习方法	无需专家制定复杂的抽取规则,降低了数据标注依赖,可利用海量未标注数据提升模型性能	准确率较差,需要人工进行抽取审核来进一步保证抽取质量

方法	优点	不足
基于医学词典和规则	根据医学词典和规则抽取,准确率高	依赖专家编写的规则和医学词典,难以适应数据不断变化的现实情况
基于机器学习方法	利用医学数据特点进行模型训练,相对简便,识别效率较高	需要高质量的数据进行训练,因而对人工标注数据的专业性要求较高
基于深度学习方法	无需专家制定复杂的抽取规则,降低了数据标注依赖,可利用海量未标注数据提升模型性能	准确率较差,需要人工进行抽取审核来进一步保证抽取质量

方法	目标
医学实体对齐	用于消除异构数据中的实体冲突,指向不一致问题,从而形成高质量知识
医学知识库融合	融合不同医学知识库以获得涵盖范围更为广泛和完整的医学知识图谱

方法	目标
医学实体对齐	用于消除异构数据中的实体冲突,指向不一致问题,从而形成高质量知识
医学知识库融合	融合不同医学知识库以获得涵盖范围更为广泛和完整的医学知识图谱

推理方式	推理概述	推理优势	推理弱势
基于逻辑规则的医学推理	基于一阶逻辑或谓词逻辑等逻辑规则进行推理	与人类的推理过程较为接近,逻辑推理能力强,并可利用人类的先验知识来进行辅助推理	对医学领域专家依赖性强,计算复杂度相对较高,泛化能力较差
基于表示学习的医学推理	主要是学习三元组中实体和关系的低维实值嵌入表示,然后基于此向量化的嵌入表示进行计算和推理	计算方便,可以充分利用知识图谱中的结构信息。对于大规模医学知识图谱的学习和推理效果较好	建模时不能引入先验知识来实现推理,对多跳推理效果较差
基于深度学习的医学推理	基于深度学习模型的优势对实体和关系进行学习,然后通过知识图谱中的结构信息和路径信息建立特征预测模型进行相关推理	无需数据标注,强大的特征捕捉能力,路径序列搜索在一定程度上提高了医学推理的可解释性	模型难以训练,长路径推理效果较差

医学知识推理研究现状与发展

Research and Development of Medical Knowledge Graph Reasoning

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