Mining health knowledge graph for health risk prediction

Tao, Xiaohui; Pham, Thuan; Zhang, Ji; Yong, Jianming; Goh, Wee Pheng; Zhang, Wenping; Cai, Yi

doi:10.1007/s11280-020-00810-1

Cited by 34 publications

(11 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• The model given in [26] describes a method for representing symptoms in the form of knowledge graphs for health risk prediction (HRP).…”

Section: Baseline Methodsmentioning

confidence: 99%

Dynamic Spatial-Temporal Graph Model for Disease Prediction

Senthilkumar,

Gupte,

2022

IJACSA

View full text Add to dashboard Cite

Advances in the field of Neural Networks, especially Graph Neural Networks (GNNs) has helped in many fields, mainly in the areas of Chemistry and Biology where recognizing and utilising hidden patterns is of much importance. In Graph Neural Networks, the input graph structures are exploited by using the dependencies formed by the nodes. The data can also be transformed in the form of graphs which can then be used in such models. In this paper, a method is proposed to make appropriate transformations and then to use the structure to predict diseases. Current models in disease prediction do not fully use the temporal features that are associated with diseases, such as the order of the occurrence of symptoms and their significance. In the proposed work, the presented model takes into account the temporal features of a disease and represents it in terms of a graph to fully utilize the power of Graph Neural Networks and Spatial-Temporal models which take into consideration of the underlying structure that change over time. The model can be efficiently used to predict the most likely disease given a set of symptoms as input. The model exhibits the best algorithm based on its accuracy. The accuracy of the algorithm is determined by the performance on the given dataset. The proposed model is compared with the existing baseline models and proves to be outstanding and more promising in the disease prediction.

show abstract

“…• The model given in [26] describes a method for representing symptoms in the form of knowledge graphs for health risk prediction (HRP).…”

Section: Baseline Methodsmentioning

confidence: 99%

Dynamic Spatial-Temporal Graph Model for Disease Prediction

Senthilkumar,

Gupte,

2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…Modeling food domain KGs were also implemented in [60][61][62]. Further, tackling challenges in healthcare systems leveraging KGs technologies was discussed in [63][64][65].…”

Section: Healthcarementioning

confidence: 99%

Domain-specific knowledge graphs: A survey

Abu-Salih

2021

Journal of Network and Computer Applications

244

View full text Add to dashboard Cite

“…Domain-specific KG can be combined with different industries to provide corresponding solutions for specific scenarios. In recent years, Domain-specific KG has been successfully applied in medical, finance, e-commerce and other industries [31][32][33][34][35].…”

Section: Kg and Its Applications In The Risk Fieldmentioning

confidence: 99%

Risk Assessment of Alpine Skiing Events Based on Knowledge Graph: A Focus on Meteorological Conditions

Wang

Zhang

Feng

et al. 2021

IJGI

View full text Add to dashboard Cite

The alpine skiing event is particularly vulnerable to changes in meteorological conditions as a winter sport held outdoors. The commonly used risk assessment methods cannot be inflexible and cannot be dynamically adjusted to combine multiple risk factors and actual conditions. A knowledge graph can organize data resources in the risk domain as structured knowledge systems. This paper combines a knowledge graph and risk assessment to effectively assess the risk status. First of all, we introduce the relevant literature review of sports event risk assessment, combining the characteristics of alpine skiing events. Then, we summarize the risk types of alpine skiing events and related risk knowledge. Secondly, a model is proposed to introduce an event risk assessment model based on the RippleNet framework combined with the characteristics of large-scale sports events. Moreover, the validity of the model is verified. The results show that the RippleNet-based event risk assessment model can be used to assess the risk of alpine skiing events. In order to effectively deal with the large-scale sports events that occur with a variety of risks, the smooth implementation of large-scale sports events provides a strong guarantee.

show abstract

Mining health knowledge graph for health risk prediction

Cited by 34 publications

References 42 publications

Dynamic Spatial-Temporal Graph Model for Disease Prediction

Dynamic Spatial-Temporal Graph Model for Disease Prediction

Domain-specific knowledge graphs: A survey

Risk Assessment of Alpine Skiing Events Based on Knowledge Graph: A Focus on Meteorological Conditions

Contact Info

Product

Resources

About