Geriatric Disease Reasoning Based on Knowledge Graph

Feng, Shaobin; Ning, Huansheng; Yang, Shunkun; Zhao, Dongmei

doi:10.1007/978-981-15-1925-3_33

Cited by 2 publications

(4 citation statements)

References 12 publications

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“…The RIP can be expressed as below. Knowlife KG [50] 7 13 50 000 609 322 MKG [47] 9 -22 508 579 094 HG [31] -14 400 000 200 000 SMR KG [66] 4 4 367 201 1 707 609 Disease oriented CLKG [127] --200 000 1 000 000 Geriatric KG [26] 6 7 --Depression KG [79] ---8 892 722 Knee osteoarthritis KG [65] 8 10 2518 29 972 SemKG [92] 5 4 --DSTKG [130] 7 16 9868 11 005 Cancer KG [15] ---90 673 527 KGHC [98] 10 22 5028 13 296 Rare disease KG [94] 10 42-3 819 623 84 223 681 COVID-KOP [27] --45 300 5 532 000 DRKF KG [49] -43 12 497 165 901 LT-D DB [51] 3 11 --KDKG [13] ---10 146 311 StrokeKG [91] 9 30 46 000 157 000 KGPA [71] 11 10 --…”

Section: Reasoning Over Mkgmentioning

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%

“…The construction of an MKG for all fields is very challenging. Therefore, the main research is currently focused on the direction led by particular application scenarios [26][27][28][29][30] .…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: Reasoning Over Mkgmentioning

confidence: 99%

Section: Intelligent Applications Of Mkgmentioning

confidence: 99%

Section: Disease Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

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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Novel graph-based machine-learning technique for viral infectious diseases: application to influenza and hepatitis diseases

Alqaissi,

Alotaibi,

Sher Ramzan

et al. 2023

Annals of Medicine

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Background Most infectious diseases are caused by viruses, fungi, bacteria and parasites. Their ability to easily infect humans and trigger large-scale epidemics makes them a public health concern. Methods for early detection of these diseases have been developed; however, they are hindered by the absence of a unified, interoperable and reusable model. This study seeks to create a holistic and real-time model for swift, preliminary detection of infectious diseases using symptoms and additional clinical data. Materials and methods In this study, we present a medical knowledge graph (MKG) that leverages multiple data sources to analyse connections between different nodes. Medical ontologies were used to enhance the MKG. We applied various graph algorithms to extract key features. The performance of multiple machine-learning (ML) techniques for influenza and hepatitis detection was assessed, selecting multi-layer perceptron (MLP) and random forest (RF) models due to their superior outcomes. The hyperparameters of both graph-based ML models were automatically fine-tuned. Results Both the graph-based MLP and RF models showcased the least loss and error rates, along with the most specific, accurate recall, precision and F 1 scores. Their Matthews correlation coefficients were also optimal. When compared with existing ML techniques and findings from the literature, these graph-based ML models manifested superior detection accuracy. Conclusions The graph-based MLP and RF models effectively diagnosed influenza and hepatitis, respectively. This underlines the potential of graph data science in enhancing ML model performance and uncovering concealed relationships in the MKG.

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Geriatric Disease Reasoning Based on Knowledge Graph

Cited by 2 publications

References 12 publications

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

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

Novel graph-based machine-learning technique for viral infectious diseases: application to influenza and hepatitis diseases

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