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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| Vydáno v: | Information processing & management Ročník 57; číslo 6; s. 102324 |
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| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Oxford
Elsevier Ltd
01.11.2020
Elsevier Science Ltd |
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| ISSN: | 0306-4573, 1873-5371 |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| ArticleNumber | 102324 |
| Author | Zhou, Rui Wang, Ye Zhang, Han Sheng, Ming Dong, Jing Xing, Chunxiao Zhang, Yong Han, Guangjie |
| Author_xml | – sequence: 1 givenname: Yong surname: Zhang fullname: Zhang, Yong organization: BNRist, DCST, RIIT, Tsinghua University, Beijing, 100084, China – sequence: 2 givenname: Ming surname: Sheng fullname: Sheng, Ming email: shengming@tsinghua.edu.cn organization: BNRist, DCST, RIIT, Tsinghua University, Beijing, 100084, China – sequence: 3 givenname: Rui surname: Zhou fullname: Zhou, Rui email: rzhou@swin.edu.au organization: Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia – sequence: 4 givenname: Ye surname: Wang fullname: Wang, Ye organization: Center for Science&Technology Talents, MoST, Beijing, 100045, China – sequence: 5 givenname: Guangjie surname: Han fullname: Han, Guangjie organization: Hohai University, Changzhou, 213022, China – sequence: 6 givenname: Han surname: Zhang fullname: Zhang, Han organization: University of Pittsburgh, Pittsburgh, 15260, US – sequence: 7 givenname: Chunxiao surname: Xing fullname: Xing, Chunxiao organization: BNRist, DCST, RIIT, Tsinghua University, Beijing, 100084, China – sequence: 8 givenname: Jing surname: Dong fullname: Dong, Jing organization: University of Queensland, QLD, 4072, Australia |
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| Keywords | Health knowledge graph health informatics clinician-in-the-loop platform knowledge island healthcare |
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