Obesity is a critical health condition that severely affects an individual’s quality of life andwell-being.The occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diab...Obesity is a critical health condition that severely affects an individual’s quality of life andwell-being.The occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diabetes,hypertension,and some types of cancer.Therefore,it is vital to avoid obesity and or reverse its occurrence.Incorporating healthy food habits and an active lifestyle can help to prevent obesity.In this regard,artificial intelligence(AI)can play an important role in estimating health conditions and detecting obesity and its types.This study aims to see obesity levels in adults by implementing AIenabled machine learning on a real-life dataset.This dataset is in the form of electronic health records(EHR)containing data on several aspects of daily living,such as dietary habits,physical conditions,and lifestyle variables for various participants with different health conditions(underweight,normal,overweight,and obesity type I,II and III),expressed in terms of a variety of features or parameters,such as physical condition,food intake,lifestyle and mode of transportation.Three classifiers,i.e.,eXtreme gradient boosting classifier(XGB),support vector machine(SVM),and artificial neural network(ANN),are implemented to detect the status of several conditions,including obesity types.The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods,achieving overall performance rates of 98.5%and 99.6%in the scenarios explored.展开更多
Electronic health network （EHN） is an information system providing functions involved in e-health. In this paper, we devise mechanisms covering three important security and privacy issues of EHN including trust mana...Electronic health network （EHN） is an information system providing functions involved in e-health. In this paper, we devise mechanisms covering three important security and privacy issues of EHN including trust management, privacy preserving, and data sharing. First, we propose an authenticated key agreement scheme based on hierarchical identity-based signature （HIBS）. We abstract a hierarchical architecture from the social network architecture of EHN. To support large-scale scenarios, we introduce a virtual signature generation phase into traditional HIBS, thus our scheme will be efficient even the depth is quite big. Second, we propose a fast data searching scheme based on symmetric searchable encryption （SSE）. To improve the searching efficiency, we introduce a two-level cache structure into the traditional SSE. Third, we propose an access control scheme based on hierarchical identity- based encryption （HIBE）. To make it a fine-grained scheme, we organize the data owner＇s file in hierarchy and introduce a virtual key generation phase to traditional HIBE. Also, the scheme can provide delegation and revocation functions easily, Besides, our schemes guarantee known-key secrecy, forward secrecy, and antidirection secrecy and possess the resistance capability to collude-attack. Evaluation results show that our scheme indeed achieves the security and efficiency.展开更多
Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions. As clinical data are complex and var...Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions. As clinical data are complex and varied in their structure and components, continued innovation of modelling strategies is required to identify architectures that can best model outcomes. In this work, we trained a Heterogeneous Graph Model(HGM) on electronic health record(EHR) data and used the resulting embedding vector as additional information added to a Convolutional Neural Network(CNN) model for predicting in-hospital mortality. We show that the additional information provided by including time as a vector in the embedding captured the relationships between medical concepts, lab tests, and diagnoses, which enhanced predictive performance. We found that adding HGM to a CNN model increased the mortality prediction accuracy up to 4%. This framework served as a foundation for future experiments involving different EHR data types on important healthcare prediction tasks.展开更多
随着移动设备的发展和普及,基于体域网(Body Area Network,BAN)的电子健康记录正变得越来越流行。人们将从体域网中获取的医疗数据备份到云端,导致几乎任何地方的医疗人员都能够使用移动终端来访问用户的医疗数据。但是对于一些病患来说...随着移动设备的发展和普及,基于体域网(Body Area Network,BAN)的电子健康记录正变得越来越流行。人们将从体域网中获取的医疗数据备份到云端,导致几乎任何地方的医疗人员都能够使用移动终端来访问用户的医疗数据。但是对于一些病患来说,这些医疗数据属于个人隐私,他们只想让拥有某些权限的人查看。文中提出了一种高效、安全的细粒度访问控制方案,不仅实现了授权用户对云存储中医疗数据的访问,而且还支持某些特权医生对记录进行修改。为了提高整个系统的效率,加入了先匹配再解密的手段,用于执行解密测试而不解密。此外,该方案将双线性配对操作外包给网关,而不会泄露数据内容,因此在很大程度上消除了用户的解密开销。性能评估显示所提解决方案在计算、通信和存储方面的效率得到了显著提高。展开更多
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia,for this research through a grant(NU/IFC/ENT/01/020)under the Institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘Obesity is a critical health condition that severely affects an individual’s quality of life andwell-being.The occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diabetes,hypertension,and some types of cancer.Therefore,it is vital to avoid obesity and or reverse its occurrence.Incorporating healthy food habits and an active lifestyle can help to prevent obesity.In this regard,artificial intelligence(AI)can play an important role in estimating health conditions and detecting obesity and its types.This study aims to see obesity levels in adults by implementing AIenabled machine learning on a real-life dataset.This dataset is in the form of electronic health records(EHR)containing data on several aspects of daily living,such as dietary habits,physical conditions,and lifestyle variables for various participants with different health conditions(underweight,normal,overweight,and obesity type I,II and III),expressed in terms of a variety of features or parameters,such as physical condition,food intake,lifestyle and mode of transportation.Three classifiers,i.e.,eXtreme gradient boosting classifier(XGB),support vector machine(SVM),and artificial neural network(ANN),are implemented to detect the status of several conditions,including obesity types.The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods,achieving overall performance rates of 98.5%and 99.6%in the scenarios explored.
基金Supported by the National Key Basic Research Program(973 program)(2012CB315905)the National Natural Science Foundation of China(61272501)Beijing Natural Science Foundation(4132056)
文摘Electronic health network （EHN） is an information system providing functions involved in e-health. In this paper, we devise mechanisms covering three important security and privacy issues of EHN including trust management, privacy preserving, and data sharing. First, we propose an authenticated key agreement scheme based on hierarchical identity-based signature （HIBS）. We abstract a hierarchical architecture from the social network architecture of EHN. To support large-scale scenarios, we introduce a virtual signature generation phase into traditional HIBS, thus our scheme will be efficient even the depth is quite big. Second, we propose a fast data searching scheme based on symmetric searchable encryption （SSE）. To improve the searching efficiency, we introduce a two-level cache structure into the traditional SSE. Third, we propose an access control scheme based on hierarchical identity- based encryption （HIBE）. To make it a fine-grained scheme, we organize the data owner＇s file in hierarchy and introduce a virtual key generation phase to traditional HIBE. Also, the scheme can provide delegation and revocation functions easily, Besides, our schemes guarantee known-key secrecy, forward secrecy, and antidirection secrecy and possess the resistance capability to collude-attack. Evaluation results show that our scheme indeed achieves the security and efficiency.
文摘Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions. As clinical data are complex and varied in their structure and components, continued innovation of modelling strategies is required to identify architectures that can best model outcomes. In this work, we trained a Heterogeneous Graph Model(HGM) on electronic health record(EHR) data and used the resulting embedding vector as additional information added to a Convolutional Neural Network(CNN) model for predicting in-hospital mortality. We show that the additional information provided by including time as a vector in the embedding captured the relationships between medical concepts, lab tests, and diagnoses, which enhanced predictive performance. We found that adding HGM to a CNN model increased the mortality prediction accuracy up to 4%. This framework served as a foundation for future experiments involving different EHR data types on important healthcare prediction tasks.
文摘随着移动设备的发展和普及,基于体域网(Body Area Network,BAN)的电子健康记录正变得越来越流行。人们将从体域网中获取的医疗数据备份到云端,导致几乎任何地方的医疗人员都能够使用移动终端来访问用户的医疗数据。但是对于一些病患来说,这些医疗数据属于个人隐私,他们只想让拥有某些权限的人查看。文中提出了一种高效、安全的细粒度访问控制方案,不仅实现了授权用户对云存储中医疗数据的访问,而且还支持某些特权医生对记录进行修改。为了提高整个系统的效率,加入了先匹配再解密的手段,用于执行解密测试而不解密。此外,该方案将双线性配对操作外包给网关,而不会泄露数据内容,因此在很大程度上消除了用户的解密开销。性能评估显示所提解决方案在计算、通信和存储方面的效率得到了显著提高。
