With the rapid growth in the availability of digital health-related data,there is a great demand for the utilization of intelligent information systems within the healthcare sector.These systems can manage and manipul...With the rapid growth in the availability of digital health-related data,there is a great demand for the utilization of intelligent information systems within the healthcare sector.These systems can manage and manipulate this massive amount of health-related data and encourage different decision-making tasks.They can also provide various sustainable health services such as medical error reduction,diagnosis acceleration,and clinical services quality improvement.The intensive care unit(ICU)is one of the most important hospital units.However,there are limited rooms and resources in most hospitals.During times of seasonal diseases and pandemics,ICUs face high admission demand.In line with this increasing number of admissions,determining health risk levels has become an essential and imperative task.It creates a heightened demand for the implementation of an expert decision support system,enabling doctors to accurately and swiftly determine the risk level of patients.Therefore,this study proposes a fuzzy logic inference system built on domain-specific knowledge graphs,as a proof-of-concept,for tackling this healthcare-related issue.The system employs a combination of two sets of fuzzy input parameters to classify health risk levels of new admissions to hospitals.The proposed system implemented utilizes MATLAB Fuzzy Logic Toolbox via several experiments showing the validity of the proposed system.展开更多
This work presents the design of an Internet of Things(IoT)edge-based system based on model transformation and complete weighted graph to detect violations of social distancing measures in indoor public places.Awirele...This work presents the design of an Internet of Things(IoT)edge-based system based on model transformation and complete weighted graph to detect violations of social distancing measures in indoor public places.Awireless sensor network based on Bluetooth Low Energy is introduced as the infrastructure of the proposed design.A hybrid model transformation strategy for generating a graph database to represent groups of people is presented as a core middleware layer of the detecting system’s proposed architectural design.A Neo4j graph database is used as a target implementation generated from the proposed transformational system to store all captured real-time IoT data about the distances between individuals in an indoor area and answer user predefined queries,expressed using Neo4j Cypher,to provide insights from the stored data for decision support.As proof of concept,a discrete-time simulation model was adopted for the design of a COVID-19 physical distancing measures case study to evaluate the introduced system architecture.Twenty-one weighted graphs were generated randomly and the degrees of violation of distancing measures were inspected.The experimental results demonstrate the capability of the proposed system design to detect violations of COVID-19 physical distancing measures within an enclosed area.展开更多
This study proposes an emergency rescue collaboration knowledge graph construction method for urban agglomeration in earthquake disasters.Based on the collection of 22 earthquake disaster emergency plans published on ...This study proposes an emergency rescue collaboration knowledge graph construction method for urban agglomeration in earthquake disasters.Based on the collection of 22 earthquake disaster emergency plans published on the official websites of multiple cities in the Chengdu-Chongqing urban agglomeration in China,earthquake disaster emergency rescue data from the Red Cross Society of Sichuan Province and Chongqing City,and historical rescue information from the China Blue Sky rescue team,this study defines and extracts six types of entities including rescue entities,policy documents,rescue actions,rescue information,rescue supplies,and emergency response levels.A knowledge graph pattern layer is established using a hybrid approach of top-down and bottom-up,including concept layer,relationship layer,rule layer,and instance layer.This study extracts earthquake disaster emergency rescue collaboration knowledge information from collected data sources,and YEDDA software is used for knowledge fusion,thus constructing a knowledge graph data layer.The data is stored in the Neo4j graph database as triplets(entity-relation-entity).Visual representation and retrieval are used to achieve the query,association,and inference of emergency rescue collaboration information for urban agglomeration in earthquake disasters.The 2022 Luding earthquake disaster in Ganzi Tibetan Autonomous Prefecture,China is selected as a typical case,and verified the effectiveness and reliability by inputting the case into the emergency rescue collaboration knowledge graph which was constructed in this study.The results indicate that the constructed knowledge graph provides intelligent decision support for earthquake disaster emergency rescue collaboration in urban agglomeration,effectively improves the performance of earthquake disaster emergency rescue,and provides new ideas and methods for earthquake disaster rescue and reduction.展开更多
基金funded by the Deanship of Scientific Research at Umm Al-Qura University,Makkah,Kingdom of Saudi Arabia.Under Grant Code:22UQU4281755DSR05.
文摘With the rapid growth in the availability of digital health-related data,there is a great demand for the utilization of intelligent information systems within the healthcare sector.These systems can manage and manipulate this massive amount of health-related data and encourage different decision-making tasks.They can also provide various sustainable health services such as medical error reduction,diagnosis acceleration,and clinical services quality improvement.The intensive care unit(ICU)is one of the most important hospital units.However,there are limited rooms and resources in most hospitals.During times of seasonal diseases and pandemics,ICUs face high admission demand.In line with this increasing number of admissions,determining health risk levels has become an essential and imperative task.It creates a heightened demand for the implementation of an expert decision support system,enabling doctors to accurately and swiftly determine the risk level of patients.Therefore,this study proposes a fuzzy logic inference system built on domain-specific knowledge graphs,as a proof-of-concept,for tackling this healthcare-related issue.The system employs a combination of two sets of fuzzy input parameters to classify health risk levels of new admissions to hospitals.The proposed system implemented utilizes MATLAB Fuzzy Logic Toolbox via several experiments showing the validity of the proposed system.
文摘This work presents the design of an Internet of Things(IoT)edge-based system based on model transformation and complete weighted graph to detect violations of social distancing measures in indoor public places.Awireless sensor network based on Bluetooth Low Energy is introduced as the infrastructure of the proposed design.A hybrid model transformation strategy for generating a graph database to represent groups of people is presented as a core middleware layer of the detecting system’s proposed architectural design.A Neo4j graph database is used as a target implementation generated from the proposed transformational system to store all captured real-time IoT data about the distances between individuals in an indoor area and answer user predefined queries,expressed using Neo4j Cypher,to provide insights from the stored data for decision support.As proof of concept,a discrete-time simulation model was adopted for the design of a COVID-19 physical distancing measures case study to evaluate the introduced system architecture.Twenty-one weighted graphs were generated randomly and the degrees of violation of distancing measures were inspected.The experimental results demonstrate the capability of the proposed system design to detect violations of COVID-19 physical distancing measures within an enclosed area.
文摘This study proposes an emergency rescue collaboration knowledge graph construction method for urban agglomeration in earthquake disasters.Based on the collection of 22 earthquake disaster emergency plans published on the official websites of multiple cities in the Chengdu-Chongqing urban agglomeration in China,earthquake disaster emergency rescue data from the Red Cross Society of Sichuan Province and Chongqing City,and historical rescue information from the China Blue Sky rescue team,this study defines and extracts six types of entities including rescue entities,policy documents,rescue actions,rescue information,rescue supplies,and emergency response levels.A knowledge graph pattern layer is established using a hybrid approach of top-down and bottom-up,including concept layer,relationship layer,rule layer,and instance layer.This study extracts earthquake disaster emergency rescue collaboration knowledge information from collected data sources,and YEDDA software is used for knowledge fusion,thus constructing a knowledge graph data layer.The data is stored in the Neo4j graph database as triplets(entity-relation-entity).Visual representation and retrieval are used to achieve the query,association,and inference of emergency rescue collaboration information for urban agglomeration in earthquake disasters.The 2022 Luding earthquake disaster in Ganzi Tibetan Autonomous Prefecture,China is selected as a typical case,and verified the effectiveness and reliability by inputting the case into the emergency rescue collaboration knowledge graph which was constructed in this study.The results indicate that the constructed knowledge graph provides intelligent decision support for earthquake disaster emergency rescue collaboration in urban agglomeration,effectively improves the performance of earthquake disaster emergency rescue,and provides new ideas and methods for earthquake disaster rescue and reduction.
