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Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease management: A New Paradigm in Smart healthcare

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Current Medical Science 2021年第6期41卷 1123-1133页

作者： Yi XIE Lin LU Fei GAO Shuang-jiang HE Hui-juan ZHAO Ying FANG Jia-ming YANG Ying AN Zhe-wei YE Zhe DONG Department of Orthopedic Surgery Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China Laboratory of Intelligent Medicine Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhan 430074China Wuhan Fourth Hospital Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430032China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China

Chronic diseases are a growing concern worldwide,with nearly 25% of adults suffering from one or more chronic health conditions,thus placing a heavy burden on individuals,families,and healthcare *** the advent of the“Smart healthcare”era,a series of cutting-edge technologies has brought new experiences to the management of chronic *** them,smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic ***,how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management,in terms of quality of life,patient outcomes,and privacy protection,is an urgent issue that needs to be *** intelligence(AI)can provide intelligent suggestions by analyzing a patient’s physiological data from wearable devices for the diagnosis and treatment of *** addition,blockchain can improve healthcare services by authorizing decentralized data sharing,protecting the privacy of users,providing data empowerment,and ensuring the reliability of data *** AI,blockchain,and wearable technology could optimize the existing chronic disease management models,with a shift from a hospital-centered model to a patient-centered *** this paper,we conceptually demonstrate a patient-centric technical framework based on AI,blockchain,and wearable technology and further explore the application of these integrated technologies in chronic disease ***,the shortcomings of this new paradigm and future research directions are also discussed.

关键词： artificial intelligence blockchain wearable technology/devices chronic diseases smart healthcare health monitoring personalization healthcare management patient-centric

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Artificial Intelligence Based Sentiment Analysis for Health Crisis management in Smart Cities

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Computers, Materials & Continua 2022年第4期71卷 143-157页

作者： Anwer Mustafa Hilal Badria Sulaiman Alfurhood Fahd N.Al-Wesabi Manar Ahmed Hamza Mesfer Al Duhayyim Huda G.Iskandar Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia

Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living andsustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install ITplatforms to collect and examine massive quantities of data. At the same time,it is essential to design effective artificial intelligence (AI) based tools to handlehealthcare crisis situations in smart cities. To offer proficient services to peopleduring healthcare crisis time, the authorities need to look closer towardsthem. Sentiment analysis (SA) in social networking can provide valuableinformation regarding public opinion towards government actions. With thismotivation, this paper presents a new AI based SA tool for healthcare crisismanagement (AISA-HCM) in smart cities. The AISA-HCM technique aimsto determine the emotions of the people during the healthcare crisis time, suchas COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides,brain storm optimization (BSO) with deep belief network (DBN), called BSODBN model is employed for feature extraction. Moreover, beetle antennasearch with extreme learning machine (BAS-ELM) method was utilized forclassifying the sentiments as to various classes. The use of BSO and BASalgorithms helps to effectively modify the parameters involved in the DBNand ELM models respectively. The performance validation of the AISA-HCMtechnique takes place using Twitter data and the outcomes are examinedwith respect to various measures. The experimental outcomes highlighted theenhanced performance of the AISA-HCM technique over the recent state ofart SA approaches with the maximum precision of 0.89, recall of 0.88, Fmeasure of 0.89, and accuracy of 0.94.

关键词： Smart city sentiment analysis artificial intelligence healthcare management metaheuristics deep learning parameter tuning

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CONTROLLING INFECTIOUS DISEASE OUTBREAKS： A DETERMINISTIC ALLOCATION-SCHEDULING MODEL WITH MULTIPLE DISCRETE RESOURCES

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Journal of Systems Science and Systems Engineering 2017年第2期26卷 219-239页

作者： Nikolaos Rachaniotis Thomas K. Dasaklis Costas Pappis Department of Economics Democritus University of Thrace Komotini Greece Department of Industrial management and Technology University of Piraeus Piraeus Greece

Infectious disease outbreaks occurred many times in the past and are more likely to happen in the future. In this paper the problem of allocating and scheduling limited multiple, identical or non-identical, resources employed in parallel, when there are several infected areas, is considered. A heuristic algorithm, based on Shih＇s （1974） and Pappis and Rachaniotis＇（2010） algorithms, is proposed as the solution methodology. A numerical example implementing the proposed methodology in the context of a specific disease outbreak, namely influenza, is presented. The proposed methodology could be of significant value to those drafting contingency plans and healthcare policy agendas.

关键词： Resource allocation healthcare management epidemics heuristics

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A dynamic risk-based early warning monitoring system for population-based management of cardiovascular disease

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Fundamental Research 2021年第5期1卷 534-542页

作者： Xiaofei Liu Qianqian Li Weiye Chen Peng Shen Yexiang Sun Qi Chen Jinguo Wu Jingyi Zhang Ping Lu Hongbo Lin Xun Tang Pei Gao Department of Epidemiology and Biostatistics Peking University Health Science CemerBeijingChina Center for Real-world Evidence evaluation Peking University Clinical Research InstituteBeijingChina Yinzhou District Center for Disease Control and Prevention NingboChina Wonders Information Co. LtdShanghaiChina Key Laboratory of Molecular Cardiovascular Sciences(Peking University) Ministry of EducationBeijingChina

Risk prediction tools are crucial for population-based management of cardiovascular disease(CVD).However,most prediction models are currently used to assess long-term risk instead of the risk of short-term CVD *** developed a Dynamic Risk-based Early wAming Monitoring(DREAM)system using large-scale,real-time electronic health record data from 2010 to 2020 from the CHinese Electronic health Records Research in Yinzhou *** dynamic risk scores were derived from a 1:5 matched nested case-control set comprising 70,470 individuals(11,745 CVD events)and then validated in a cohort of 81,205 individuals(5950 CVD events).The individuals were Chinese adults aged 40-79 years without a history of CVD at *** predictors related to vital signs,laboratory tests,and health service utilization were selected to establish the dynamic *** proposed scores were significantly associated with the subsequent CVD onset(adjusted odds ratio,1.21;95%confidence interval,1.20-1.23).The area under the receiver operating characteristic curves(AUCs)was 0.6010(0.5929-0.6092)and 0.6021(0.5937-0.6105)for the long-term 10-year CVD risk10%group in the validation set,the change in AUC in addition to the long-term risk was 0.0235(0.0155-0.0315).By increasing the risk threshold from 7 to 16 points,the proportion of true subsequent CVD cases among those given alerts increased from 40.61%to 85.31%.In terms of management efficiency,the number needed to manage per CVD case ranged from 2.46 to 1.17 using the risk *** the increasing popularity and integration of EHR systems with wearable technology,the DREAM scores can be incorporated into an early-warning system and applied in dynamic,real-time,EHR-based,automated management to support healthcare decision making for individuals,general practitioners,and policymakers.

关键词： Cardiovascular disease Electronic health records Dynamic prediction Real-time monitoring healthcare management Early-warning system

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Inpatient bed allocation policy under uncertain capacity

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Journal of management Science and Engineering 2019年第4期4卷 228-251页

作者： Xing Liu Youhua Chen Li Luo Ting Zhu Wenwu Shen TAIWO Enayon Sunday Noah’s Ark Laboratory Huawei TechnologiesShenzhen250012China Business School City University of Hong KongHong KongChina Business School Sichuan UniversitySichuan610064China West China Biomedical Big Data Center West China Hospital/West China School of MedicineSichuan UniversitySichaun610041China Outpatient Department West China HospitalSichuan UniversitySichuan610041China

This paper concerns the problem of inpatient bed allocation for two classes of patients(scheduled and non-scheduled)when there is uncertainty about daily available *** the afternoon of each day,patients from the scheduled class,also called backlogged elective admissions,are selected from a waiting list,for the admission on the next *** non-scheduled class,also called emergent admissions,are new requests that arise randomly each day with emergent *** capacity of available beds for a medical specialty to provide hospitalization services is uncertain when backlogged elective pa-tients are *** too many of elective patients may result in exceeding a day’s capacity,which can potentially necessitate"overflowing"or"postponing"some emergent requests that should be performed as soon as ***,the problem faced by the medical specialty facility at the decision-making point of each day is how many of the backlogged elective patients can be *** formulate this problem as a Markov decision process(MDP)and study the structural properties of the model to characterize the nature of the optimal *** propose easy-to-implement policies(the fixed quota policy and the best fixed quota policy),which perform well under fitted *** reporting numerical analyses using real data from a Chinese public hospital,we finally compare the improvements that our proposed solutions could bring to the hospital with the existing practices under several different cost structures.

关键词： Bed allocation Markov decision process Emergency reservation healthcare management

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Analytics on medical records collected from a distributed system deployed in the Indian rural demographic

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Journal of management Analytics 2018年第1期5卷 54-72页

作者： K.G.Srinivasa Sriram Anupindi Arvind Kumar Department of Computer Science and Engineering M S Ramaiah Institute of TechnologyBangaloreIndia CBP Government Engineering College New DelhiIndia

One of the standout problems in rural regions of India is the lack of a comprehensive medical record *** records are a vital part of any diagnosis as they provide a glimpse into the patient’s past,which is influential to the current *** records help practitioners spot anomalies and patterns in a variety of medical *** is also utilized to gain a better understanding of the health situation in certain *** this paper,the authors have proposed a comprehensive distributed system which can be used to preserve the medical history of individuals,and also provide a valuable insight into the health situation of the rural *** distributed data are aggregated into a single entity from which observations are *** data acquired could be of extreme importance to the government,as it can be utilized to determine the health issues which require immediate attention,and to evaluate possible mitigation *** data collected would be portable and easily accessible with the help of mobile devices.

关键词： data analytics distributed systems healthcare analytics electronic health record healthcare management management analytics in healthcare medical analytics

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