Improving the risk management of Type 2 diabetes mellitus in China from the perspective of social relationships

Wang, Xiao-Jia; Chen, Mi; Wei, Xiaoying; Zhang, Shanshan; Xu, Wenxiang; Guan, Yuxiang

doi:10.1111/exsy.12484

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…The purpose of this study is to analyse FSMS development using RDE of an evolutionary game and to make a modest contribution to FSMS development. Figure 1 illustrates the proposed framework of this paper (Wang, Chen, Shang, & Yang, 2018;Wang, Chen, Xia, et al, 2018). In Figure 1, we use EGT to analyse the three players' RDE.…”

Section: Replication Dynamics Equationmentioning

confidence: 99%

Diffusion strategies for face swiping medical service using evolutionary game theory

Wang

Cheng

et al. 2021

Expert Systems

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Face swiping is the action of registering facial recognition data with a biometric authentication application. Face swiping-based medical services (FSMSs) is an emerging measurement technology underpinning modern intelligent medical services in China. In view of convenience in the delivery of medical services and the potential to reduce medical costs, FSMSs have become an important part of China's medical reform. However, modelling its eventual uses has not been fully explored in theory and practice, and this is hindering the take up of FSMSs in China. In this paper, we build an evolutionary game model to explore three stakeholders-policy makers, hospitals, and patients-and their dynamic interdependence of their decision-making when adopting FSMS. Based on our game theoretic analysis, we develop a novel model to track the behaviour of these three stakeholders and analyse the corresponding payoff matrix to establish the replication dynamic equation (RDE) for the game. Further, we then use RDE to calculate the different stability points of players and determine the game's stable strategy. Finally, we validate the proposed model with a detailed simulation. Our observations may benefit not only FSMS participants but also several other forms of medical services and industries.

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Section: Replication Dynamics Equationmentioning

confidence: 99%

Diffusion strategies for face swiping medical service using evolutionary game theory

Wang

Cheng

et al. 2021

Expert Systems

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“…Gestational diabetes mellitus (GDM), which is defined as carbohydrate intolerance of a variable degree with onset or recognition during pregnancy, has recently been identified as a potential risk factor for type II diabetes mellitus (T2DM) [3][4][5]. Compared with normal women, women diagnosed with GDM are 10 times more likely to develop type 2 diabetes, 2.5 times more likely to have ischemic heart disease, and twice as likely to have hypertension during their later years [6,7].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method

Wang

Zhang

et al. 2022

Int J Comput Intell Syst

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Gestational diabetes mellitus (GDM) is the most common disease in pregnancy and can cause a series of maternal and infant complications. A new study shows that GDM affects one in six deliveries. Identifying and screening for risk factors for GDM can effectively help intervene and improve the condition of women and their children. Therefore, the aim of this paper is to determine the risk factors for GDM and to use the ensemble learning method to judge whether pregnant women suffer from GDM more accurately. First, this study involves six commonly used machine learning algorithms to analyze the GDM data from the Tianchi competition, selects the risk factors according to the ranking of each model, and uses the Shapley additive interpreter method to determine the importance of the selected risk factors. Second, the combined weighting method was used to analyze and evaluate the risk factors for gestational diabetes and to determine a group of important factors. Lastly, a new integrated light gradient-boosting machine-extreme gradient boosting-gradient boosting tree (LightGBM-Xgboost-GB) learning method is proposed to determine whether pregnant women have gestational diabetes mellitus. We used the gray correlation degree to calculate the weight and used a genetic algorithm for optimization. In terms of prediction accuracy and comprehensive effects, the final model is better than the commonly used machine learning model. The ensemble learning model is comprehensive and flexible and can be used to determine whether pregnant women suffer from GDM. In addition to disease prediction, the model can also be extended for use to many other areas of research.

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Measurement Method of Answerer Service Influence for Q & A Community

Liu

Jiang

et al. 2020

2020 International Conference on E-Commerce and Internet Technology (ECIT)

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Improving the risk management of Type 2 diabetes mellitus in China from the perspective of social relationships

Cited by 3 publications

References 36 publications

Diffusion strategies for face swiping medical service using evolutionary game theory

Diffusion strategies for face swiping medical service using evolutionary game theory

Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method

Measurement Method of Answerer Service Influence for Q & A Community

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