HKGB: An Inclusive, Extensible, Intelligent, Semi-auto-constructed Knowledge Graph Framework for Healthcare with Clinicians’ Expertise Incorporated

Health knowledge graph provides an ideal technical means to integrate heterogeneous data resources and enhance knowledge-based services. There are many challenges for the construction of health knowledge graph such as complex concepts and relationships, various medical standards, heterogeneous data...

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Veröffentlicht in:Information processing & management Jg. 57; H. 6; S. 102324
Hauptverfasser: Zhang, Yong, Sheng, Ming, Zhou, Rui, Wang, Ye, Han, Guangjie, Zhang, Han, Xing, Chunxiao, Dong, Jing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 01.11.2020
Elsevier Science Ltd
Schlagworte:
Algorithms
Biomedical materials
clinician-in-the-loop
Data integration
Data quality
Data structures
Disease
Extensibility
Graph theory
Graphs
Health
health informatics
Health information
Health knowledge graph
Health services
healthcare
Information processing
Intelligence
Knowledge
Knowledge bases (artificial intelligence)
knowledge island
Knowledge representation
Medical personnel
Osteoarthritis
platform
Prior knowledge
Workloads
Health knowledge graph
health informatics
clinician-in-the-loop
platform
knowledge island
healthcare
ISSN:0306-4573, 1873-5371
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Zusammenfassung:Health knowledge graph provides an ideal technical means to integrate heterogeneous data resources and enhance knowledge-based services. There are many challenges for the construction of health knowledge graph such as complex concepts and relationships, various medical standards, heterogeneous data structures, poor data quality, highly accurate and interpretable services, etc. In this paper, firstly, we propose Health Knowledge Graph Builder (HKGB), an end-to-end platform which could be used to construct disease-specific and extensible health knowledge graphs from multiple sources. Secondly, we analyze the capabilities and requirements of clinicians, design the tasks to involve the clinicians and implement a clinician-in-the-loop toolset to integrate the clinicians prior knowledge into the construction of health knowledge graphs. Thirdly, we design an extensible mechanism to add new diseases to an existing knowledge graph. Fourthly, we present a quantitative effort estimation algorithm to quantitatively evaluate the effort of clinicians during the construction, and use it to calculate the workloads such as 44.27 person days for knee osteoarthritis domain. Finally, we have developed several knowledge graph based tools to facilitate real applications.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0306-4573
1873-5371
DOI:10.1016/j.ipm.2020.102324