HKDP: A Hybrid Knowledge Graph Based Pediatric Disease Prediction System

Liu, Penghe; Wang, Xiaoqing; Sun, Xiaoping; Shen, Xiaodong; Xu, Chen; Sun, Yuzhong; Pan, Yue

doi:10.1007/978-3-319-59858-1_8

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…As a result, the query runtime performance increased significantly by up to 76%. Liu et al [33] structured the expertise of domain experts as (If. .…”

Section: Reasoning Based On Spmentioning

confidence: 99%

“…The use of disease diagnosis is mostly focused on the identification of particular diseases and offering prediction results for them. For example, the applications based on MKGs can provide diagnostic services for pediatric [33] and geriatric [26] diseases. Certain prevalent specialist diseases, such as type 2 diabetes, thyroid disease, subarachnoid hemorrhage, and sepsis, have garnered significant study attention.…”

Section: Intelligent Applications Of Mkgmentioning

confidence: 99%

“…Pediatric disease prediction [33] Hybrid-KG GM: HKDP: classification Geriatric disease reasoning [26] Geriatric KG SP: logic&rule inference: relation prediction Rare disease diagnosis [34] -SP: entity feature! SVM: classification Thyroid disease diagnosis [92] SemKG KRL: KGE!…”

Section: Disease Diagnosismentioning

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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“…As a result, the query runtime performance increased significantly by up to 76%. Liu et al [33] structured the expertise of domain experts as (If. .…”

Section: Reasoning Based On Spmentioning

confidence: 99%

Section: Intelligent Applications Of Mkgmentioning

confidence: 99%

See 1 more Smart Citation

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

Duan

Pan

et al. 2023

Big Data Min. Anal.

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“…However, while those approaches build a bipartite graph of symptoms and diseases, the medicalvalues knowledge graph uses more complex diagnosis pathways and rules (see below). Some approaches have been proposed in the recent years which cover particular subfields of medicine [14] or diseases [4]. In contrast to those works, the medicalvalues knowledge graph aims at covering a larger variety of diseases.…”

Section: Related Workmentioning

confidence: 99%

Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph

Heilig¹,

Kirchhoff²,

Stumpe³

et al. 2022

Preprint

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Medical diagnosis is the process of making a prediction of the disease a patient is likely to have, given a set of symptoms and observations. This requires extensive expert knowledge, in particular when covering a large variety of diseases. Such knowledge can be coded in a knowledge graph -encompassing diseases, symptoms, and diagnosis paths. Since both the knowledge itself and its encoding can be incomplete, refining the knowledge graph with additional information helps physicians making better predictions. At the same time, for deployment in a hospital, the diagnosis must be explainable and transparent. In this paper, we present an approach using diagnosis paths in a medical knowledge graph. We show that those graphs can be refined using latent representations with RDF2vec, while the final diagnosis is still made in an explainable way. Using both an intrinsic as well as an expert-based evaluation, we show that the embedding-based prediction approach is beneficial for refining the graph with additional valid conditions.

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“…In recent years, China's domain-specific knowledge graph has achieved some research productions. For example, the knowledge graph of traditional Chinese medicine published by the China Academy of Chinese Medical Sciences, which contains more than 600,000 entities and more than 12.7 million relations [27], includes the knowledge graph of traditional Chinese medicine prescription [28]; the "HKDP," which is the knowledge graph of pediatric disease prevention system [29]; etc. Other industries also have some achievements, such as the academic knowledge graph "AceKG" [30], published by Shanghai Jiao Tong University; the educational knowledge graph "KnowEdu" [31]; and so on.…”

Section: Literature Reviewmentioning

confidence: 99%

Construction of Knowledge Graphs for Maritime Dangerous Goods

et al. 2019

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Dangerous goods occupy an important proportion in international shipping, and government and enterprises pay a lot of attention to transport safety. There are a wide variety of dangerous goods, and the knowledge involved is extensive and complex. Organizing and managing this knowledge plays an important role in the safe transportation of dangerous goods. The knowledge graph is a mass of brand-new knowledge management technologies that provide powerful technical support for integrating domain knowledge and solving the problem of the “knowledge island.” This paper first introduces the knowledge of maritime dangerous goods (MDG); constructs a three-layer knowledge structure of MDG, dividing this knowledge into two categories; uses ontology to express the concepts, entities, and relations of MDG; and puts forward the representation methods of the conceptual layer and entity layer and designs them in detail. Finally, the knowledge graph of maritime dangerous goods (KGMDG) is constructed. Furthermore, we demonstrate the knowledge visualization, retrieval, and automatic judgment of segregation requirement based on KGMDG. It is proved that KGMDG does not only help to simplify the retrieval process of professional knowledge and to promote intelligent transportation but is also conducive to the sharing, dissemination, and utilization of MDG knowledge.

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HKDP: A Hybrid Knowledge Graph Based Pediatric Disease Prediction System

Cited by 11 publications

References 12 publications

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

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

Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph

Construction of Knowledge Graphs for Maritime Dangerous Goods

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