Construction of Chinese Pediatric Medical Knowledge Graph

Shen, Yu; Cai, Linkun; Zhang, Kunli; Zan, Hongying; Liu, Tao; Ren, Xiaohui

doi:10.1007/978-981-15-3412-6_21

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…These entities included but were not limited to the patient's basic information, primary complaints, tests, test findings, diagnosis, and treatment plans. Song et al [85] constructed a pediatric KG using manual annotation, knowledge fusion, and other technologies; they expanded the triplet form of knowledge to a sextuplet form. Cheng et al [60] used data mining to mine medical laws, transformed them into medical knowledge with the assistance of specialists and then constructed the KG appropriately.…”

Section: Entity and Relation Extractionmentioning

confidence: 99%

“…A "fact" is a fundamental unit of information expressed in KGs, and it is often described using the RDF framework, which is also known as a triplet. However, Li et al [47] represented medical knowledge using a quadruplet structure rather than a triplet structure, and Song et al [85] expanded the triplet form of a KG to a sextuplet form. Varied terminologies have different meanings in various description situations.…”

Section: Constructed Mkgsmentioning

confidence: 99%

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Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Duan

Pan

et al. 2023

Big Data Min. Anal.

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Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use in a variety of intelligent medical applications. Thus, understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field. To this end, we offer an in-depth review of MKG in this work. Our research begins with the examination of four types of medical information sources, knowledge graph creation methodologies, and six major themes for MKG development. Furthermore, three popular models of reasoning from the viewpoint of knowledge reasoning are discussed. A reasoning implementation path (RIP) is proposed as a means of expressing the reasoning procedures for MKG. In addition, we explore intelligent medical applications based on RIP and MKG and classify them into nine major types. Finally, we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

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Section: Entity and Relation Extractionmentioning

confidence: 99%

Section: Constructed Mkgsmentioning

confidence: 99%

Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Duan

Pan

et al. 2023

Big Data Min. Anal.

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“…In recent years, as the maturity of technology and more emphasis on healthcare, it has become more and more popular to apply knowledge graph in the medical field and has attracted much attention from researchers in computer and medical to combine these two fields [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Rotmensch et al [11] in 2017 proposed learning a health knowledge graph from electronic medical records by using probabilistic.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [25] made use of a knowledge graph to process medical archives in 2019. In addition, Song et al [13] put forward the Chinese pediatric medical knowledge graph in 2019. Ying et al [15] proposed a system for drug-drug similarity measure based on a knowledge graph in 2019.…”

Section: Introductionmentioning

confidence: 99%

Medical Knowledge Graph in Chinese Using Deep Semantic Mobile Computation Based on IoT and WoT

Liu

Yin

Wang

et al. 2021

Wireless Communications and Mobile Computing

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In this paper, a novel medical knowledge graph in Chinese approach applied in smart healthcare based on IoT and WoT is presented, using deep neural networks combined with self-attention to generate medical knowledge graph to make it more convenient for performing disease diagnosis and providing treatment advisement. Although great success has been made in the medical knowledge graph in recent studies, the issue of comprehensive medical knowledge graph in Chinese appropriate for telemedicine or mobile devices have been ignored. In our study, it is a working theory which is based on semantic mobile computing and deep learning. When several experiments have been carried out, it is demonstrated that it has better performance in generating various types of medical knowledge graph in Chinese, which is similar to that of the state-of-the-art. Also, it works well in the accuracy and comprehensive, which is much higher and highly consisted with the predictions of the theoretical model. It proves to be inspiring and encouraging that our work involving studies of medical knowledge graph in Chinese, which can stimulate the smart healthcare development.

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Specific Relation Attention-Guided Graph Neural Networks for Joint Entity and Relation Extraction in Chinese EMR

2022

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Electronic medical records (EMRs) contain a variety of valuable medical entities and their relations. The extraction of medical entities and their relations has important application value in the structuring of EMR and the development of various types of intelligent assistant medical systems, and hence is a hot issue in intelligent medicine research. In recent years, most research aims to firstly identify entities and then to recognize the relations between the entities, and often suffers from many redundant operations. Furthermore, the challenge remains of identifying overlapping relation triplets along with the entire medical entity boundary and detecting multi-type relations. In this work, we propose a Specific Relation Attention-guided Graph Neural Networks (SRAGNNs) model to jointly extract entities and their relations in Chinese EMR, which uses sentence information and attention-guided graph neural networks to perceive the features of every relation in a sentence and then to extract those relations. In addition, a specific sentence representation is constructed for every relation, and sequence labeling is performed to extract its corresponding head and tail entities. Experiments on a medical evaluation dataset and a manually labeled Chinese EMR dataset show that our model improves the performance of Chinese medical entities and relation extraction.

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Construction of Chinese Pediatric Medical Knowledge Graph

Cited by 3 publications

References 8 publications

Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Medical Knowledge Graph in Chinese Using Deep Semantic Mobile Computation Based on IoT and WoT

Specific Relation Attention-Guided Graph Neural Networks for Joint Entity and Relation Extraction in Chinese EMR

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