Health Data Availability Protection:Delta-XOR-Relay Data Update in Erasure-Coded Cloud Storage Systems

To achieve the high availability of health data in erasure-coded cloud storage systems,the data update performance in erasure coding should be continuously optimized.However,the data update performance is often bottlenecked by the constrained cross-rack bandwidth.Various techniques have been proposed in the literature to improve network bandwidth efficiency,including delta transmission,relay,and batch update.These techniques were largely proposed individually previously,and in this work,we seek to use them jointly.To mitigate the cross-rack update traffic,we propose DXR-DU which builds on four valuable techniques:(i)delta transmission,(ii)XOR-based data update,(iii)relay,and(iv)batch update.Meanwhile,we offer two selective update approaches:1)data-deltabased update,and 2)parity-delta-based update.The proposed DXR-DU is evaluated via trace-driven local testbed experiments.Comprehensive experiments show that DXR-DU can significantly improve data update throughput while mitigating the cross-rack update traffic.

Health Data Deduplication Using Window Chunking-Signature Encryption in Cloud

Due to the development of technology in medicine,millions of health-related data such as scanning the images are generated.It is a great challenge to store the data and handle a massive volume of data.Healthcare data is stored in the cloud-fog storage environments.This cloud-Fog based health model allows the users to get health-related data from different sources,and duplicated informa-tion is also available in the background.Therefore,it requires an additional sto-rage area,increase in data acquisition time,and insecure data replication in the environment.This paper is proposed to eliminate the de-duplication data using a window size chunking algorithm with a biased sampling-based bloomfilter and provide the health data security using the Advanced Signature-Based Encryp-tion(ASE)algorithm in the Fog-Cloud Environment(WCA-BF+ASE).This WCA-BF+ASE eliminates the duplicate copy of the data and minimizes its sto-rage space and maintenance cost.The data is also stored in an efficient and in a highly secured manner.The security level in the cloud storage environment Win-dows Chunking Algorithm(WSCA)has got 86.5%,two thresholds two divisors(TTTD)80%,Ordinal in Python(ORD)84.4%,Boom Filter(BF)82%,and the proposed work has got better security storage of 97%.And also,after applying the de-duplication process,the proposed method WCA-BF+ASE has required only less storage space for variousfile sizes of 10 KB for 200,400 MB has taken only 22 KB,and 600 MB has required 35 KB,800 MB has consumed only 38 KB,1000 MB has taken 40 KB of storage spaces.

Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.

Stand-Alone Patient Reception and Referral System with Health Data Management

The COVID-19 pandemic has exposed vulnerabilities within our healthcare structures. Healthcare facilities are often faced with staff shortages and work overloads, which can have an impact on the collection of health data and constants essential for early diagnosis. In order to minimize the risk of error and optimize data collection, we have developed a robot incorporating artificial intelligence. This robot has been designed to automate and collect health data and constants in a contactless way, while at the same time verifying the conditions for correct measurements, such as the absence of hats and shoes. Furthermore, this health information needs to be transmitted to services for processing. Thus, this article addresses the aspect of reception and collection of health data and constants through various modules: for taking height, temperature and weight, as well as the module for entering patient identification data. The article also deals with orientation, presenting a module for selecting the patient’s destination department. This data is then routed via a wireless network and an application integrated into the doctors’ tablets. This application will enable efficient queue management by classifying patients according to their order of arrival. The system’s infrastructure is easily deployable, taking advantage of the healthcare facility’s local wireless network, and includes encryption mechanisms to reinforce the security of data circulating over the network. In short, this innovative system will offer an autonomous, contactless method for collecting vital constants such as size, mass, and temperature. What’s more, it will facilitate the flow of data, including identification information, across a network, simplifying the implementation of this solution within healthcare facilities.

Blockchain application in healthcare service mode based on Health Data Bank

Blockchain is commonly considered a potentialdisruptive technology. Moreover, the healthcareindustry has experienced rapid growth in the adoption ofhealth information technology, such as electronic healthrecords and electronic medical records. To guarantee dataprivacy and data security as well as to harness the value ofhealth data, the concept of Health Data Bank (HDB) isproposed. In this study, HDB is defined as an integratedhealth data service institution, which bears no “ownership”of health data and operates health data under the principalagentmodel. This study first comprehensively reviews themain characters of blockchain and identifies the blockchain-based healthcare industry projects and startups in theareas of health insurance, pharmacy, and medical treatment.Then, we analyze the fundamental principles ofHDB and point out four challenges faced by HDB’ssustainable development: (1) privacy protection andinteroperability of health data;(2) data rights;(3) healthdata supervision;(4) and willingness to share health data.We also analyze the important benefits of blockchainadoption in HDB. Furthermore, three application scenariosincluding distributed storage of health data, smart-contractbasedhealthcare service mode, and consensus-algorithmbasedincentive policy are proposed to shed light on HDBbasedhealthcare service mode. In the end, this study offersinsights into potential research directions and challenges.

Addressing the Security Challenges of Big Data Analytics in Healthcare Research

Big data and associated analytics have the potential to revolutionize healthcare through the tools and techniques they offer to manage and exploit the large volumes of heterogeneous data being collected in the healthcare domain. The strict security and privacy constraints on this data, however, pose a major obstacle to the successful use of these tools and techniques. The paper first describes the security challenges associated with big data analytics in healthcare research from a unique perspective based on the big data analytics pipeline. The paper then examines the use of data safe havens as an approach to addressing the security challenges and argues for the approach by providing a detailed introduction to the security mechanisms implemented in a novel data safe haven. The CIMVHR Data Safe Haven (CDSH) was developed to support research into the health and well-being of Canadian military, Veterans, and their families. The CDSH is shown to overcome the security challenges presented in the different stages of the big data analytics pipeline.

Hydraulic metal structure health diagnosis based on data mining technology

In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.

The State of the Art of Data Science and Engineering in Structural Health Monitoring

Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion.

An enrichment model using regular health examination data for early detection of colorectal cancer

Objective: Challenges remain in current practices of colorectal cancer(CRC) screening, such as low compliance,low specificities and expensive cost. This study aimed to identify high-risk groups for CRC from the general population using regular health examination data.Methods: The study population consist of more than 7,000 CRC cases and more than 140,000 controls. Using regular health examination data, a model detecting CRC cases was derived by the classification and regression trees(CART) algorithm. Receiver operating characteristic(ROC) curve was applied to evaluate the performance of models. The robustness and generalization of the CART model were validated by independent datasets. In addition, the effectiveness of CART-based screening was compared with stool-based screening.Results: After data quality control, 4,647 CRC cases and 133,898 controls free of colorectal neoplasms were used for downstream analysis. The final CART model based on four biomarkers(age, albumin, hematocrit and percent lymphocytes) was constructed. In the test set, the area under ROC curve(AUC) of the CART model was 0.88 [95%confidence interval(95% CI), 0.87-0.90] for detecting CRC. At the cutoff yielding 99.0% specificity, this model’s sensitivity was 62.2%(95% CI, 58.1%-66.2%), thereby achieving a 63-fold enrichment of CRC cases. We validated the robustness of the method across subsets of test set with diverse CRC incidences, aging rates, genders ratio, distributions of tumor stages and locations, and data sources. Importantly, CART-based screening had the higher positive predictive value(1.6%) than fecal immunochemical test(0.3%).Conclusions: As an alternative approach for the early detection of CRC, this study provides a low-cost method using regular health examination data to identify high-risk individuals for CRC for further examinations. The approach can promote early detection of CRC especially in developing countries such as China, where annual health examination is popular but regular CRC-specific screening is rare.

An Efficiency Assessment of Tuberculosis Treatment on Health Centers: A Data Envelopment Analysis Approach

This study utilized Data Envelopment Analysis (DEA) in assessing the efficiency of health center in tuberculosis (TB) treatment. Assessing the efficiency of health center treating TB is a vital and sensitive topic, because there is a cumulative amount of public funds devoted to healthcare. In this research, a DEA model has been correlated to evaluate and assess the efficiency of 17 health centers. The researchers selected the health budget and the number of health workers as input variables likewise, the number of people served, number of TB patients served, and TB patients treated (%) as output variables. Based on the result of the study, only five (5) health centers out of seventeen (17) have 100% efficiencies throughout the 2 years period. It is recommended that other health centers should learn from their efficient peers recognized by the DEA model so as to increase the overall performance of the healthcare system. Likewise, health centers should integrate Health Information Technology to deliver healthier care for their patients.

Assessing the Relative Efficiency of Health Systems in Sub-Saharan Africa Using Data Envelopment Analysis

We assess the relative efficiency of health systems of 35 countries in sub-Saharan Africa using Data Envelopment Analysis. This method allows us to evaluate the ability of each country to transform its sanitary “inputs” into health “outputs”. Our results show that, on average, the health systems of these countries have an efficiency score between 72% and 84% of their maximum level. We also note that education and density of population are factors that affect the efficiency of the health system in these countries.

Hybrid Smart Contracts for Securing IoMT Data

Data management becomes essential component of patient healthcare.Internet of Medical Things(IoMT)performs a wireless communication between E-medical applications and human being.Instead of consulting a doctor in the hospital,patients get health related information remotely from the physician.The main issues in the E-Medical application are lack of safety,security and priv-acy preservation of patient’s health care data.To overcome these issues,this work proposes block chain based IoMT Processed with Hybrid consensus protocol for secured storage.Patients health data is collected from physician,smart devices etc.The main goal is to store this highly valuable health related data in a secure,safety,easy access and less cost-effective manner.In this research we combine two smart contracts such as Practical Byzantine Fault Tolerance with proof of work(PBFT-PoW).The implementation is done using cloud technology setup with smart contracts(PBFT-PoW).The accuracy rate of PBFT is 90.15%,for PoW is 92.75%and our proposed work PBFT-PoW is 99.88%.

Assessment of Knowledge and Practices of Community Health Nurses on Data Quality in the Ho Municipality of Ghana

Background: High data quality provides correct and up-to-date information which is critical to ensure, not only for the maintenance of health care at an optimal level, but also for the provision of high-quality clinical care, continuing health care, clinical and health service research, and planning and management of health systems. For the attainment of achievable improvements in the health sector, good data is core. Aim/Objective: To assess the level of knowledge and practices of Community Health Nurses on data quality in the Ho municipality, Ghana. Methods: A descriptive cross-sectional study was employed for the study, using a standard Likert scale questionnaire. A census was used to collect 77 Community Health Nurses’ information. The statistical software, Epi-Data 3.1 was used to enter the data and exported to STATA 12.0 for the analyses. Chi-square and logistic analyses were performed to establish associations between categorical variables and a p-value of less than 0.05 at 95% significance interval was considered statistically significant. Results: Out of the 77 Community Health Nurses studied, 49 (63.64%) had good knowledge on data accuracy, 51 (66.23%) out of the 77 Community Health Nurses studied had poor knowledge on data completeness, and 64 (83.12%) had poor knowledge on data timeliness out of the 77 studied. Also, 16 (20.78%) and 33 (42.86%) of the 77 Community Health Nurses responded there was no designated staff for data quality review and no feedback from the health directorate respectively. Out of the 16 health facilities studied for data quality practices, half (8, 50.00%) had missing values on copies of their previous months’ report forms. More so, 10 (62.50%) had no reminders (monthly data submission itineraries) at the facility level. Conclusion: Overall, the general level of knowledge of Community Health Nurses on data quality was poor and their practices for improving data quality at the facility level were woefully inadequate. Therefore, Community Health Nurses need to be given on-job training and proper education on data quality and its dimensions. Also, the health directorate should intensify its continuous supportive supervisory visits at all facilities and feedback should be given to the Community Health Nurses on the data submitted.

桥梁健康监测数据的质量评估方法研究

桥梁健康监测数据的挖掘和分析工作只有在整体数据质量符合基本要求的有效数据基础上进行,才能保障如模态参数识别、损伤识别和状态评估等后续工作的准确性。因此,基于量化改进的探索性分析方法(Exploratory Data Analysis,EDA)和相关性分析从数据完整性、准确性和一致性的角度建立了桥梁健康监测静、动态数据的质量评估方法。对某大跨度斜拉桥健康监测系统的静、动态数据进行质量评估,通过对比分析了不同评估质量的温度数据、静挠度数据和不同评估质量的主梁竖向加速度动力信号的模态参数识别的稳定图,验证了所提方法的正确性。结果表明,所提评估方法能够快速有效地判断数据质量的好坏,进而确保桥梁结构的服役性能评估和预测的准确性,有利于提高健康监测数据的可用性和效能。

基于fsQCA组态视角的我国医疗资源配置效率提升路径分析

背景当前我国医疗资源配置区域失衡、公平性缺失问题仍然突出。党的“二十大”报告明确提出要“促进优质医疗资源扩容和区域均衡布局”。目的探究提升我国医疗资源配置效率的具体路径,为实现我国医疗资源合理且高效配置、促进基本公共服务均等化提供科学参考。方法于2022年9月—2023年2月开展研究,数据源于《2021中国统计年鉴》《2021中国卫生健康统计年鉴》。将医疗卫生机构数、卫生技术人员数、床位数作为投入指标,以诊疗人次和入院人数为产出指标,通过数据包络分析(DEA)方法对我国2020年31个省份医疗资源配置效率进行测度;以医疗资源配置效率为结果变量,以卫生技术人员占比、每千人口床位数、出院者平均住院日、人均国内生产总值(GDP)、居民可支配收入、财政收入分权和医疗卫生财政预算支出占比为条件变量,运用模糊集定性比较分析(fsQCA)方法从组态视角探究内外部要素对医疗资源配置效率的协同影响机制,剖析高或非高水平医疗资源配置效率的条件组态,明确医疗资源高效率和低效率配置的多重路径。结果2020年我国31个省份医疗资源配置效率整体水平较高,均值为0.852,但省际存在较大差异。组态分析结果可知,医疗资源配置效率的提升是多因素共同作用的结果,共存在3种医疗资源高效率配置的路径。路径1:政府主导型驱动路径,以广西壮族自治区为典型案例。路径2:内外协调型驱动路径,以云南省和甘肃省为代表案例。路径3:均衡型驱动路径,代表案例主要有广东省、福建省和湖北省。非高医疗资源配置效率的路径也存在3条。路径1:政府制约型路径。路径2:经济-政府双重制约型路径,代表案例有黑龙江省和吉林省。路径3:内外制约型路径,典型案例有山西省和西藏自治区。结论内外部各要素及要素间的协同在良性互动过程中共同影响着医疗资源配置效率水平,需优化内外部环境,并对关键资源要素进行有效整合,以形成合力,促进区域医疗资源合理配置。

机器学习在网络健康资料质量评估中的研究进展

随着互联网技术的发展,涌现了大量网络健康资料,但这些资料质量参差不齐,可靠性和可读性有待提高。如何评估网络健康资料质量,成为困扰医护人员和患者的现实问题。机器学习在海量数据分析中的优势作用,为高效评估网络健康资料质量提供了可能。本文将从机器学习在网络健康资料可靠性、可读性等方面的研究进展进行综述,为编写精准可读的健康教育资料提供参考。

专利计量视域下医疗与健康保险机构间数据交互技术探析

目的/意义探析相关专利技术,为医疗与商业健康保险机构打通数据交互堵点、建构多层次医疗保障体系提供经验借鉴。方法/过程采用专利计量方法,围绕相关专利技术的时间趋势、地域分布、类别分布、文本聚类4方面分析医疗与健康保险机构之间的数据交互专利技术。结果/结论中国应重视数据交互在医疗与健康保险机构深化合作中的作用,增进高质量专利的申报、授权、运用和保护,重视跨界融合与技术驱动,创新专利布局以顺应技术发展和社会需求等。

基于工业互联网和区块链的“健康钱包”数据开放共享模式研究

现有健康数据共享模式面临效率低下、缺乏监管等诸多问题,无法满足国家对健康数据合规开放共享的要求,工业互联网和区块链技术的发展和应用为健康数据开放共享提供了新的路径。本研究通过引入这两种技术构建个人健康数据钱包,结合工业互联网高效的分布式数据管理及不依赖第三方公信力的特点,建立基于数据钱包的数据开放共享模式,实现对健康数据的可信流转,使患者能够更好地掌握和分享其健康信息。结果表明该数据开放共享模式可满足“数据不出域、可用不可见”要求,将健康数据所有权归还个人,有助于提高医疗服务连续性,能够为健康数据开放共享应用提供有益借鉴。

Providing Physical and Mental Health Support Using Medical Examination Data and Perceived Health

Without ascertaining workers’ perceived health, it is difficult to achieve behavioral modification even if health guidance is conducted. To investigate physical and mental health support emphasizing “positive health,” we used the Total Health Index (THI) survey with the purpose of elucidating the association between medical examination data and perceived health. After obtaining medical examination data from 90 men, we analyzed their responses to the THI survey. The results suggested that age and abnormal medical examination data are associated with physical and mental complaints. In the analysis by age group, we found that men in their 20s had more complaints of irregularity of daily life on the THI scale. The group who responded that they were not getting enough sleep had higher mean values of total cholesterol and fasting blood sugar. The group who responded that their meals were irregular had higher mean values of Body Mass Index, aspartate aminotransferase, and alanine aminotransferase. As confirmed by the THI, continuously supporting lifestyle improvement is important. The THI of the “health guidance” group indicated fewer physical health complaints and more aggression/extroversion than the “normal” group. In those for whom health guidance was applicable, participants who were “obese” and “hypertensive” had more aggression/extroversion and lesser extent of nervousness. Based on these findings, it was suggested that meaningful, personalized health support can be developed.