Health Big Data Research Articles

Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM2.5 in 22 cities in China

Many studies have confirmed that PM2.5 exposure can cause a variety of diseases. Because people spend most of their time indoors, exposure to PM2.5 in indoor environments is critical to population health. Large-population, long-term, continuous, and accurate indoor PM2.5 data are important but scarce because of the difficulties in monitoring the indoor air quality on a large scale. Model simulation provides a new research direction. In this study, an advanced machine learning model was constructed using environmental health big data to predict the daily indoor PM2.5 concentration data in 22 typical air pollution cities in China from 2013 to 2017. The test R2 value of this model reached as high as 0.89, and the RMSE of the model was 9.13. The predicted annual indoor PM2.5 concentrations of the cities ranged from 54.6μg/m3 to 82.7μg/m3, and showed a decreasing trend year by year. The pollution level exceeds the recommended AQG level of PM2.5 and has potential impact on human health. The results could take a breakthrough in obtaining accurate big data of indoor PM2.5 and contribute to research on the indoor air quality and human health in China. SynopsisThis study established a machine learning model and predicted indoor PM2.5 big data, which could support the research of indoor PM2.5 and health.

Artificial Intelligence And Big Data For Enhancing Public Health Surveillance And Disease Prevention: A Systematic Review

This systematic review explores the transformative role of Artificial Intelligence (AI) and Big Data analytics in enhancing public health outcomes, focusing on key areas such as disease surveillance, resource allocation, and personalized preventive healthcare. In the wake of increasing healthcare challenges, the integration of AI technologies and Big Data offers unprecedented opportunities for improving health monitoring, early disease detection, and strategic resource management. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive review was conducted across multiple databases, resulting in an initial pool of 450 articles. After applying rigorous inclusion and exclusion criteria, a total of 90 high-quality studies were systematically analyzed. The findings demonstrate that AI models, particularly those leveraging machine learning, significantly enhance the early detection of outbreaks and optimize healthcare resource allocation, especially during health crises like the COVID-19 pandemic. Additionally, the use of predictive analytics in personalized preventive healthcare has shown promise in reducing the burden of chronic diseases by identifying at-risk populations and tailoring interventions based on individual risk profiles. However, challenges related to data quality, standardization, and ethical concerns continue to hinder the widespread adoption of these technologies. The review emphasizes the need for interdisciplinary collaboration and robust data governance frameworks to fully realize the potential of AI and Big Data in public health. This study not only highlights current advancements but also identifies gaps in research, offering insights into future directions for integrating AI-driven solutions to strengthen public health systems globally.

Development and validation of a relatively accurate gastric cancer high-risk group screening scoring system in urban residents.

Our study aimed to develop a relatively accurate gastric cancer (GC) screening score system for urban residents and to validate the screening efficacy. The present study included a derivation cohort (n = 3406) and a validation cohort (n = 868) of urban residents. Applying the full-stack engineering intelligent system platform of Hualian Health Big Data of Shandong University, the clinical physical examination data of subjects were collected. Univariate and multivariate analyses were used to identify risk factors for GC, and subsequently, an optimal prediction rule was established to create three distinct scoring systems. In the GC-risk scoring system I, age, plateletocrit (PCT), carcinoembryonic antigen (CEA), glucose, albumin, creatinine were independent risk factors of GC, with scores ranging from 0 to 28 and optimal cut-off was 15.5. The second scoring system consisted of age, PCT, RDW-CV, CEA, glucose, albumin, and creatinine, with scores ranging from 0 to 31. The optimal cut-off point was determined to be 15.5. The scoring system III comprise of age, sex, PCT, RDW CV, CEA, glucose, with scores ranging from 0 to 21 and optimal cut-off was 10.5. All three scoring systems demonstrated excellent discrimination for GC, achieving an AUC of 0.884, 0.89, and 0.876, respectively. In external validation, the AUC values were 0.654, 0.658, and 0.714. Notably, the GC-risk scoring system III exhibited the highest screening efficiency. Urban residents benefited from the effective and verified GC-risk scoring systems, which demonstrated excellent performance in identifying individuals with an elevated risk of GC.

Establishment and application of infectious disease monitoring, early warning and disposal system

Using big data and artificial intelligence to establish a multi-point monitoring, early warning, and disposal system to achieve early warning and intervention of infectious disease outbreaks is an important means of controlling the spread of the epidemic. Taking Xiaoshan district as an example, this study analyzes the monitoring contents, warning methods, and application effectiveness of the infectious disease monitoring, early warning and disposal system. Based on Xiaoshan's health big data resources, the system starts with syndrome, disease diagnosis and etiology. Through advanced technologies such as artificial intelligence and block chain, it realizes early identification of infectious disease outbreaks, data fusion, multi-cross collaboration, and closed-loop management. It has improved the sensitivity of clustered outbreaks monitoring and the effectiveness of epidemic disposal and provided a reference for grassroots disease prevention and control departments to establish an infectious disease monitoring and early warning system.

Readiness of big health data analytics by technology-organization-environment (TOE) framework in Ethiopian health sectors

BackgroundBig health data is a large and complex dataset that the health sector has collected and stored continuously to generate healthcare evidence for intervening the future healthcare uncertainty. However, data use for decision-making practices has been significantly low in developing countries, especially in Ethiopia. Hence, it is critical to ascertain which elements influence the health sector's decision to adopt big health data analytics in health sectors. The aim of this study was to identify the level of readiness for big health data analytics and its associated factors in healthcare sectors. MethodsA cross-sectional study design was conducted among 845 target employees using the structural equation modeling approach by using technological, organizational, and environmental (TOE) frameworks. The target population of the study was health sector managers, directors, team leaders, healthcare planning officers, ICT/IT managers, and health professionals. For data analysis, exploratory factor analysis using SPSS 20.0 and structural equation modeling using AMOS software were used. Result58.85 % of the study participants had big health data analytics readiness. Complexity (CX), Top management support (TMS), training (TR) and government law policies and legislation (GLAL) and government IT policies (GITP) had positive direct effect, compatibility (CT), and optimism (OP) had negative direct effect on BD readiness (BDR) ConclusionThe technological, organizational, and environmental factors significantly contributed to big health data readiness in the healthcare sector. The Complexity, compatibility, optimism, Top management support, training (TR) and government law and IT policies (GITP) had effect on big health data analytics readiness. Formulating efficient reform in healthcare sectors, especially for evidence-based decision-making and jointly working with stakeholders will be more relevant for effective implementation of big health data analytics in healthcare sectors.

Generalized Denoising Coder with Multi-modal Approach for Handling Incomplete Information in Public Health Big Data Analytics

A data-driven health model incorporates many missing and incomplete values, and the effectiveness of a health model that relies on characteristics not captured by the consumer is low. A Deep Learning (DL) health paradigm aims to improve the precision of learning weights. A DL-based health model is trained by optimizing the weights to maximize accuracy. If the learning process is excluded, the accuracy of applying a DL-based health model to the user's circumstance may be reduced, and it becomes the responsibility of people to manage Public Health Big Data Records (PHBDR). To handle incomplete information, this study suggested a generalized denoising coder with a multi-modal approach in public health big data records (GDC-MM-PHBDR). The suggested technique uses a Generalized Denoising Coder (GDC), which estimates the output values following the relevant input values using Neural Networks (NN). The National Health and Nutrition Examination Study (NHNES) provided data for this investigation. This approach allows for the estimation of incomplete information in the PHBDR. Multimodality, which the PHBDRs provide, enables compiling data for a single item from many sources. The layout of the GDC is multi-modal. The GDC-MM-PHBDR approach continuously demonstrates excellent performance, attaining maximum accuracy across all noise levels. It begins at 98.31% accuracy with a noise factor of 0.05 and significantly decreases to 94.5% with a noise factor of 0.25.

Research on privacy protection in the context of healthcare data based on knowledge map.

With the rapid development of emerging information technologies such as artificial intelligence, cloud computing, and the Internet of Things, the world has entered the era of big data. In the face of growing medical big data, research on the privacy protection of personal information has attracted more and more attention, but few studies have analyzed and forecasted the research hotspots and future development trends on the privacy protection. Presently, to systematically and comprehensively summarize the relevant privacy protection literature in the context of big healthcare data, a bibliometric analysis was conducted to clarify the spatial and temporal distribution and research hotspots of privacy protection using the information visualization software CiteSpace. The literature papers related to privacy protection in the Web of Science were collected from 2012 to 2023. Through analysis of the time, author and countries distribution of relevant publications, we found that after 2013, research on the privacy protection has received increasing attention and the core institution of privacy protection research is the university, but the countries show weak cooperation. Additionally, keywords like privacy, big data, internet, challenge, care, and information have high centralities and frequency, indicating the research hotspots and research trends in the field of the privacy protection. All the findings will provide a comprehensive privacy protection research knowledge structure for scholars in the field of privacy protection research under the background of health big data, which can help them quickly grasp the research hotspots and choose future research projects.

Advancing geospatial preconception health research in primary care through medical informatics and artificial intelligence

Established life course approaches suggest that health status in adulthood can be influenced by events that occurred during the prenatal developmental period. Yet, interventions such as diet and lifestyle changes performed during pregnancy have had a small impact on both maternal and offspring health outcomes. Currently, there is a growing body of literature that highlights the importance of maternal health before conception (months or years before pregnancy occurs) for the future health of offspring. While some studies have explored factors such as maternal body composition, nutrition, and lifestyle in this area, location-based environmental and socioeconomic exposures before conception may also contribute to future offspring health. In this line, the study of a patient's geographic history presents a promising avenue. To foster research in this direction, the integration of geospatial health, medical informatics and artificial intelligence techniques offers great potential. Importantly, novel sources of big health data sets such as electronic health records registered at the primary care level provide a unique framework due to its inherent longitudinal nature. Nonetheless, a number of privacy, ethical, and technical challenges need to be overcome for this kind of longitudinal analysis to mature and succeed. In the long-term, we support the vision of incorporating a patient's geographic history into her clinical history to equip clinicians with useful contextual information to explore.

Exploring Cancer Incidence Trends by Age and Sex Among 14.14 Million Individuals in China From 2007 to 2021: Population-Based Study.

Sex is a crucial factor in the development, progression, and treatment of cancer, making it vital to examine cancer incidence trends by sex for effective prevention strategies. This study aimed to assess the incidence of cancer in China between 2007 and 2021, with a focus on sex-based trends. A population-based cancer registry comprising 14.14 million individuals was maintained between 2007 and 2021 by the Beijing Municipal Health Big Data and Policy Research Center. The age-standardized rates (ASRs) of cancers were calculated using the Segi population. The average annual percentage of change (AAPC) was evaluated using the joinpoint regression model, while the Bayesian age-period-cohort model was used to predict cancer incidence in the next 10 years. From 2007 to 2021, the study included 651,342 incident patients with cancer, of whom 51.2% (n=333,577) were women. The incidence indicated by the ASR for all cancers combined was 200.8 per 100,000 for women and 184.4 per 100,000 for men. The increase in incidence indicated by AAPC for all malignancies combined significantly increased in women between 2007 and 2021 (AAPC=3.1%; P<.001), whereas it remained constant in men (AAPC=0.3%; P=.30). Although the overall incidence of all cancers indicated by AAPC increased in young men (AAPC=3.2%; P=.01), the greatest increase was observed among young women (AAPC=6.1%; P<.001). The incidence rate ratio for cancer in women increased among subsequent younger generations compared with patients born in the 1962-1966 cohort. The ASR in women will increase 1.6-fold over the next 10 years, with women having twice the incidence rate of men by 2031. The rising incidence of cancer among women in China has become a growing concern, emphasizing the need for increased efforts in cancer prevention and early screening, especially among young women.

Opportunities and Challenges of Utilising Big Data on SATUSEHAT Platform in Nursing: A Literature Review

The SATUSEHAT platform which is Indonesia Health Services (IHS) is a form of digital transformation initiated by the Indonesian Ministry of Health. This platform integrates the health data of all Indonesian people including medical records across health facilities which will develop into a national health data center and has the opportunity to become big health data for the Government. To face this, the nursing field must prepare for various opportunities and challenges. Objective: To provide an overview of the opportunities and challenges of utilizing big data through the SATUSEHAT platform in the nursing field. The method used in this research is literature review. The keywords used were Big data in nursing and The Application of big data for the future. Literature searches were conducted using the online database method starting from 2015-2022 through ScienceDirect, ProQuest and Clinical Key. As a result of the literature review with a case study design, it was found that opportunities for big data utilization include personalized care, nursing research development, data-based policy formulation, while challenges include increasing data processing and management competencies, integrating big data education curriculum, privacy and ethical issues and standardizing nursing terminology Recommendations It is hoped that further research can discuss the preparations that must be made to maximize the potential for big data utilization in the future.

Medical Practitioner-Centric Heterogeneous Network Powered Efficient E-Healthcare Risk Prediction on Health Big Data

From a Licensed Medical Practitioner’s (LMP) perspective, e-Healthcare Risk Prediction plays a vital role in Health Big Data. This also is a hot issue in e-healthcare because of the lack of security and privacy protections. To overcome this deficiency, this research article proposes heterogeneous network systems (HNS), an efficient and privacy-preserving e-Healthcare Risk Prediction method for e-healthcare. In comparison to the existing research contribution, the proposed HNS accomplish two steps of disease risk prediction, namely Analysis of HNS, and Heterogeneous Network (HetNet) concerning the LMP for analyzing the in-hospital involvement care by collecting and explaining the “Health Big Data” as per the view of the LMP. This will help to access the services from the hospital. In the LMP-Centric Heterogeneous Network Powered Efficient e-Healthcare Risk Prediction phase, the “Polygenic Score” is calculated for risk prediction for health big data. Through the characteristics of “non-predictive applications” and “Predictive applications,” procedural aspects are analyzed with the LMP-Centric HetNet against the Efficient e-Healthcare Risk Prediction. This will be applied to the Medical extensive data integration and clustering for handling Health Big Data. Finally, the LMP-Centric HetNet Powered Efficient e-Healthcare Risk Prediction for Health Big Data treats the LMP perspective efficiently. The proposed system increased prediction accuracy to 45.9%, and the monogenic score increased from 3% to 19%. The density accuracy range is increased from 13.9% to 39%. The increased execution time is improved from 29.95% to 36.05%. This comprehensive prediction analysis accuracy range is 73.98% efficient.

How Socio-economic Inequalities Cluster People with Diabetes in Malaysia: Geographic Evaluation of Area Disparities Using a Non-parameterized Unsupervised Learning Method

Accurate assessments of epidemiological associations between health outcomes and routinely observed proximal and distal determinants of health are fundamental for the execution of effective public health interventions and policies. Methods to couple big public health data with modern statistical techniques offer greater granularity for describing and understanding data quality, disease distributions, and potential predictive connections between population-level indicators with areal-based health outcomes. This study applied clustering techniques to explore patterns of diabetes burden correlated with local socio-economic inequalities in Malaysia, with a goal of better understanding the factors influencing the collation of these clusters. Through multi-modal secondary data sources, district-wise diabetes crude rates from 271,553 individuals with diabetes sampled from 914 primary care clinics throughout Malaysia were computed. Unsupervised machine learning methods using hierarchical clustering to a set of 144 administrative districts was applied. Differences in characteristics of the areas were evaluated using multivariate non-parametric test statistics. Five statistically significant clusters were identified, each reflecting different levels of diabetes burden at the local level, each with contrasting patterns observed under the influence of population-level characteristics. The hierarchical clustering analysis that grouped local diabetes areas with varying socio-economic, demographic, and geographic characteristics offer opportunities to local public health to implement targeted interventions in an attempt to control the local diabetes burden.

Retracted: Storage Method for Medical and Health Big Data Based on Distributed Sensor Network

Retracted: Storage Method for Medical and Health Big Data Based on Distributed Sensor Network

INTEGRATING ARTIFICIAL INTELLIGENCE AND BIG DATA IN MOBILE HEALTH: A SYSTEMATIC REVIEW OF INNOVATIONS AND CHALLENGES IN HEALTHCARE SYSTEMS

This systematic review explores the integration of Artificial Intelligence (AI) and Big Data in mobile health (mHealth) and their transformative impact on healthcare systems. Analysing 25 peer-reviewed articles, the review delves into the utilisation, innovations, and inherent challenges of AI and Big Data in various healthcare settings. The findings reveal that AI and Big Data significantly enhance diagnostic precision, personalise treatment strategies, and streamline healthcare operations. However, this technological advancement is not without its complexities. Key challenges identified include ethical dilemmas, data privacy and security issues, and preserving the human element within healthcare. The review underscores the risk of an over-reliance on AI and the criticality of using unbiased, representative data sets. The integration of AI into clinical practices, while promising, demands rigorous oversight and ethical governance. The review concludes that the successful adoption of AI and Big Data in healthcare hinges on a balanced approach that harmonises technological innovation with ethical, equitable, and human-centred healthcare practices. If managed effectively, this integration offers the potential for significant improvements in patient outcomes and operational efficiency yet requires vigilant management of the challenges to realise its benefits fully.

Conception on Public Health Surveillance System Based on Big Data in Health and Medical Care

In recent years, public health events in varying degrees have occurred in different parts of China, these public health emergencies are characterized by volatility and complexity. To effectively respond to major challenges, we must establish a comprehensive public health surveillance system capable of efficiently collecting, analyzing, and processing data, while providing effective and timely information support. This paper focuses on how big data technologies can be used to achieve this goal. The theoretical basis is first introduced, including the definition, characteristics and application methods of big data; then the existing public health surveillance system is analyzed and summarized, its shortcomings are pointed out; finally, a design scheme for a public health monitoring system based on health care big data is proposed, detailing the design ideas, functional modules and database structure of the scheme. The scheme can help the authorities to monitor and manage information in real time so as to provide the basis for scientific-decision making. Through testing and verification, the scheme is found to have good performance and can meet the needs of large-scale public health surveillance. The findings of this project hold significant relevance in advancing the development of the public health surveillance system in China.

BIAS AND GENERALIZABILITY IN MEDICARE-BASED ANALYSES ACROSS THREE POLICY PERIODS

Abstract Administrative Big Health data such as that drawn from the Medicare program is an invaluable resource for the study of population health. Although the Medicare program provides near universal health coverage for older U.S. adults age 65+, for most types of care data is collected only for individuals enrolled in traditional Medicare (TM) plans, with those enrolled in private Medicare Advantage (MA) plans being effectively censored in terms of the extent of available data. This presents two potential problems for health outcomes research: bias and generalizability. In this study we use Health and Retirement Study data (1998-2015) to assess the presence and magnitude of group-specific differences between MA and TM beneficiaries as well as plan switchers across three policy-specific periods (Balanced Budget Act; Medicare Modernization Act; Affordable Care Act). We found that the MA population was characterized by higher diversity, adverse behavioral habits, lower education, and economic disadvantage. Initially, lower morbidity levels converged with the TM average over time. Individuals, switching into MA after TM had much larger morbidity levels and mortality risk than any other Medicare group. We found significant loss of generalizability with respect to the Hispanic population; such estimates could be further biased to the extent that MA Hispanics differ from TM Hispanics. Of great concern is the combination of low education and low economic status in MA beneficiaries – a combination also found in many disadvantaged minorities. Care is needed in the design of TM-based studies, especially those focused on ethnic differences.

How the National Health Insurance Coverage policy changed the use of lenvatinib for adult patients with advanced hepatocellular carcinoma: a retrospective cohort analysis with real world big data

BackgroundTo establish a long-term mechanism to control the cost burden of drugs, the Chinese government organized seven rounds of price negotiations for the national reimbursement drug list (NRDL) from 2016 to the end of 2022. The study aimed to evaluate the impact of the National Health Insurance Coverage (NHIC) policy on the use of lenvatinib as the first-line treatment for advanced hepatocellular carcinoma (HCC) within a specific medical insurance region from the micro perspective of individual patient characteristics.MethodsThe data of HCC patients that received lenvatinib from September 2019 to August 2022 was retrieved from the Medical and Health Big Data Center and longitudinally analyzed. Contingency table chi-square statistics and binary logistic regression analysis were used to compare the differences in the categorical variables. Interrupted time-series (ITS) regression analysis was performed to evaluate the changes in the utilization of lenvatinib over 36 months. Multiple linear regression was used to analyze the impact of receiving lenvatinib on the total hospitalization expenses of hospitalized patients with advanced HCC.ResultsA total of 12,659 patients with advanced HCC were included in this study. The usage rate of lenvatinib increased from 6.19% to 15.28% over 36 months (P < 0.001). By controlling the other factors, consistent with this, the probability of patients with advanced HCC receiving lenvatinib increased by 2.72-fold after the implementation of the NHIC policy (OR = 2.720, 95% CI:2.396–3.088, P < 0.001). Older, residency in rural areas, lack of fixed income, treatment at hospitals below the tertiary level, and coverage by urban-rural residents’ basic medical insurance (URRBMI) were the factors affecting the use of lenvatinib among patients with advanced HCC (P < 0.05). After the implementation of the NHIC policy, the total hospitalization expenses increased (Beta=-0.040, P < 0.001). However, compared to patients who received lenvatinib, the total hospitalization expenses were higher for those who did not receive the drug (US$5022.07 ± US$5488.70 vs. US$3701.63 ± US$4330.70, Beta = 0.062, P < 0.001).ConclusionsThe NHIC policy has significantly increased the utilization of lenvatinib. In addition, we speculate that establishing multi-level medical insurance systems for economically disadvantaged patients would be beneficial in improving the effectiveness of the NHIC policy in the real world.

Public Health Governance Policy Optimization Based on Health Big Data

The application of healthcare big data has great significance for changing the healthcare model, enhancing service efficiency, improving service quality, and meeting people’s needs. With the development of information technology and the popularization of intelligent devices, healthcare big data will usher in a new period of application development, and the public health governance policy system will also enter a new stage of improvement and optimization. This paper combines the core functions of health big data in public health governance, and puts forward a path of optimization for public health governance policies with a focus on health big data, which can help to enhance the digitalization and comprehensive competitiveness of public health governance.

An empirical study of the impact of biological information dissemination in social media on public science literacy

Abstract In this paper, we first establish a locally converged bioinformatics dataset based on gradient sampling and design an optimal data mining control model to improve the accuracy of bioinformatics big data feature mining. The performance of the Compressive Tracking algorithm and Online Bosting algorithm is compared with the mining error as a test index. At the same time, we propose a social media information dissemination algorithm applicable to large-scale social network datasets, taking the degree value of each node as the node’s full influence and comparing and analyzing the dissemination influence of BP-IM, RAND and MC-CELF algorithms. Finally, taking public health big data as the research object, the least squares regression method was used to analyze the influence of the amount of public attention to bioinformatics scientific knowledge on their scientific literacy in different media. The results showed that there was a significant positive correlation between scientific literacy and willingness to engage in science participation behavior on social media when the amount of public attention to scientific information was β =0225, p &lt;0.01. When more people are interested in scientific knowledge of bioinformatics on social media, their scientific literacy will improve.

Prioritization of Injury Prevention and Management Programs and Research and Development (R&D) Projects: Survey Using the Delphi Technique and Analytic Hierarchy Process.

We prioritized injury prevention and management programs, and research and development (R&D) projects using the Delphi method and analytic hierarchy process (AHP) for national plan. We summarized 21 programs and 31 R&D projects based on opinions collected in the opinion survey and brainstorming. Expert panel evaluated program and project relevance in the two Delphi rounds to make consensus. Total scores were calculated by adding criterion weights calculated through the AHP multiplied by the average of scores for each program and project. The top three programs were "Integrated Management System for Injury Data," "Comprehensive Injury Surveillance System," and "Transport System Improvement Program for Major Trauma." The top three R&D projects were researches on "Identification of Vulnerable Workers Injuries to Strengthen Protections," "Data Integration on Injury and Disability," and "Using Public Health Big Data to Identify Injury Status and Risk Factors." Experts highly rated the programs to improve injury surveillance system.