Big Data In Healthcare Research Articles

Securing big healthcare data using attribute and honey-based encryption in cloud environment

Securing big healthcare data using attribute and honey-based encryption in cloud environment

Epidemiological and clinical characteristics of hepatocellular carcinoma in Xiamen

ObjectiveTo investigate the epidemiological characteristics of hepatocellular carcinoma in Xiamen and offer recommendations for its control and prevention. Methods: Data obtained from liver cancer screening in 2016–2018 and 2019–2020 in patients with hepatocellular carcinoma in Xiamen were collected using Xiamen Healthcare Big Data Platform. Epidemiological characteristics were analyzed descriptively by different years. Results: From 2016–2020, there were 3740 patients with hepatocellular carcinoma in Xiamen. In the patients, the percentage of males was higher than that of females (83.13 % vs 16.87 %). From 2019–2020, there was an increase in the detection rates for disorders including hepatitis B, hypertension, diabetes, fatty liver and cirrhosis of the liver, and there was a decrease in the prevalence of hepatitis C. There was an increase in the values of all hematological indicators of infection in patients with liver cancer in 2019–2020 compared with the previous three years. From 2016–2020, there was a year-on-year increase in the prevalence of liver cancer and fatty liver. From 2018–2020, there was an increasing trend in the prevalence of liver cancer in overweight patients in the population with fatty liver. Conclusion: In 2019–2020, there was an increase in the prevalence of chronic diseases in patients with hepatocellular carcinoma. Comprehensive liver cancer screening provides an important guide for the prevention, early diagnosis and treatment of liver cancer and for clinicians to assess patients’ condition.

Research Trends and Hotspots of Big Data in Ophthalmology: A Bibliometric Analysis and Visualization

ABSTRACT Purpose The burst of modern information has significantly promoted the development of global medicine into a new era of big data healthcare. Ophthalmology is one of the most prominent medical specialties driven by big data analytics. This study aimed to describe the development status and research hotspots of big data in ophthalmology. Methods English articles and reviews related to big data in ophthalmology published from January 1, 1999, to April 30, 2024, were retrieved from the Web of Science Core Collection. The relevant information was analyzed and visualized using VOSviewer and CiteSpace software. Results A total of 406 qualified documents were included in the analysis. The annual number of publications on big data in ophthalmology reached a rapidly increasing stage since 2019. The United States (n = 147) led in the number of publications, followed by India (n = 77) and China (n = 69). The L.V. Prasad Eye Institute in India was the most productive institution (n = 50), and Anthony Vipin Das was the most influential author with the most relevant literature (n = 45). The electronic medical records were the primary source of ophthalmic big data, and artificial intelligence served as the principal analytics tool. Diabetic retinopathy, glaucoma, and myopia are currently the main topics of interest in this field. Conclusions The application of big data in ophthalmology has experienced rapid growth in recent years. Big data is expected to play an increasingly significant role in shaping the future of research and clinical practice in ophthalmology.

The possibilities of big data in pharmacoepidemiology, problems of use, legal regulation

The growth of digitalization in medicine has significantly expanded the possibilities of using big data in pharmacoepidemiology. The use of big data makes it possible to reduce the cost of clinical research, increase the speed of recruitment and expand the sample, makes it possible to evaluate the effect of drugs in pregnant women and effectiveness in rare diseases. The databases PubMed, Scopus, Web of Science and Google Scholar for 12 years from 2012 to 2023, United Nations documents, World Health Organization, Federal laws of the Russian Federation in the field of artificial intelligence, protection of confidential information, and clinical research were analyzed. The search was carried out by keywords: «big data», «registers», «pharmacoepidemiology», «personal data», «legal regulation», «protection methods». The article provides examples of the use of big data in healthcare, including from 25 to 50 million people. The analysis of the literature data revealed the same type of problems – the lack of uniformity in the introduction of information, incomplete information, limited availability. The review identifies the problems of protecting the confidentiality of information. The mechanisms of information standardization, storage, and data processing are considered. The international and Russian legislative framework regulating the conduct of clinical trials using big data is presented.

Health data science course for clinicians: Time to bridge the skills gap?

Data science skills are highly relevant for clinicians working in an era of big data in healthcare. However, these skills are not routinely taught, representing a growing unmet educational need. This education report presents a structured short course that was run to teach clinicians data science and the lessons learnt. A 1-day introductory course was conducted within a tertiary hospital in London. It consisted of lectures followed by facilitated pair programming exercises in R, an object-oriented programming language. Feedback was collated and participant responses were graded using a Likert scale. The course was attended by 20 participants. The majority of participants (69%) were in higher speciality cardiology training. While more than half of the participants (56%) received prior training in statistics either through formal taught programmes (e.g., a Master's degree) or online courses, the participants reported several barriers to expanding their skills in data science due to limited programming skills, lack of dedicated time, training opportunities and awareness. After the short course, there was a significant increase in participants' self-rated confidence in using R for data analysis (mean response; before the course: 1.69 ± 1.0, after the course: 3.2 ± 0.9, p = .0005) and awareness of the capabilities of R (mean response; before the course: 2.1 ± 0.9, after the course: 3.6 ± 0.7, p = .0001, on a 5-point Likert scale). This proof-of-concept study demonstrates that a structured short course can effectively introduce data science skills to clinicians and supports future educational initiatives to integrate data science teaching into medical education.

Finding the new potential research on diabetic kidney disease and hemodialysis in healthcare insurance databases: A bibliometric analysis

To the best of our knowledge, bibliometric analysis has not been performed for studies related to diabetic kidney disease (DKD) and hemodialysis using healthcare big data. Herein, the aim of this bibliometric analysis was to identify emerging research trends in DKD and hemodialysis within healthcare insurance databases by exploring authors, co-author networks, and countries to discover new potential research areas. A bibliometric study was conducted, utilizing data obtained from the Scopus database. Keywords such as diabetic kidney disease, hemodialysis, insurance or big data, and prediction were employed. Inclusion criteria were original articles and review articles written in English published between 2010 and 2022. VOSviewer and the Bibliometrix package in R were used for comprehensive bibliometric analysis. VOSviewer facilitated keyword co-occurrence analysis to identify clusters and visualize relationships among keywords, emphasizing distinct research themes, keyword density, and network visualization. Meanwhile, Bibliometrix allowed exploration of key metrics such as prolific authors and institutions, publication trends, co-authorship networks, citations, document types, emerging trends through keyword analysis, and network visualizations, including co-authorship and keyword co-occurrence. Results from both tools were integrated for a thorough analysis. The present study yielded 2,199 articles, which was reduced to 1,828 after removing duplicates and applying inclusion criteria. This bibliometric analysis found that machine learning and artificial intelligence are emerging yet remain relatively under-researched in the context of hemodialysis and DKD. The prominence of topics such as diabetic nephropathy, non-insulin treatments, and lifestyle modifications highlighted ongoing research priorities in DKD and hemodialysis. Taiwan's dominance in publications suggested robust research activity in this field, while international collaboration underscored global interest and the potential for diverse research perspectives. The need for similar research development in Indonesia, leveraging big data and machine learning, indicates opportunities for advancing the understanding and management of DKD and hemodialysis within the region.

Association of weight change with all-cause and cause-specific mortality: an age-stratified analysis

BackgroundThe associations of weight change with all-cause and cause-specific mortality stratified by age remains unclear. We evaluated the age-stratified (< 65 vs ≥ 65 years) associations of weight change with all-cause and cause-specific mortality in a large sample of Chinese adults.MethodsOur cohort study included 746,991 adults aged at least 45 years from the Shenzhen Healthcare Big Data Cohort in China. BMI change were categorized as change within 5% (stable), decrease by 5% to 10%, decrease by > 10%, increase by 5% to 10%, and increase by > 10%. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause, non-communicable disease, cardiovascular disease (CVD), and cancer mortality according to BMI change, with adjustment for potential confounders.ResultsDuring a median follow-up of 2.2 years (2,330,180 person-years), there were 10,197 deaths. A notable interaction emerged between weight change and age. For participants ≥ 65 years, compared with stable BMI, more than a 10% decrease in BMI was associated with higher risk of all-cause mortality (HR: 1.69, 95% CI: 1.54–1.86), non-communicable disease mortality (HR: 1.67, 95% CI: 1.52–1.84), CVD mortality (HR: 1.55, 95% CI: 1.34–1.80), and cancer mortality (HR: 1.59, 95% CI: 1.33–1.92). Similar patterns of results for 5% to 10% decrease in BMI were observed. More than a 10% increase in BMI was associated with increased risk of all-cause mortality (HR: 1.13, 95% CI: 1.04–1.24), non-communicable disease mortality (HR: 1.14, 95% CI: 1.04–1.25), and CVD mortality (HR: 1.27, 95% CI: 1.12–1.44). For participants < 65 years, only more than a 10% decrease in BMI was associated with higher risk of all-cause mortality (HR: 1.41, 95% CI: 1.12–1.77), non-communicable disease mortality (HR: 1.43, 95% CI: 1.13–1.81), and cancer mortality (HR: 1.79, 95% CI: 1.29–2.47).ConclusionsWeight loss and excessive weight gain were associated with increased risks of mortality among older adults, while only excessive weight loss was associated with increased risks of mortality among middle-aged adults.

Racial and Ethnic Disparities in Predictive Accuracy of Machine Learning Algorithms Developed Using a National Database for 30-Day Complications Following Total Joint Arthroplasty

BackgroundWhile predictive capabilities of machine learning (ML) algorithms for hip and knee total joint arthroplasty (TJA) have been demonstrated in previous studies, their performance in racial and ethnic minority patients has not been investigated. This study aimed to assess the performance of ML algorithms in predicting 30-day complications following TJA in racial and ethnic minority patients. MethodsA total of 267,194 patients undergoing primary TJA between 2013 and 2020 were identified from a national outcomes database. The patient cohort was stratified according to race, with further sub-stratification into Hispanic or non-Hispanic ethnicity. There were two ML algorithms, histogram-based gradient boosting (HGB), and random forest (RF), that were modeled to predict 30-day complications following primary TJA in the overall population. They were subsequently assessed in each racial and ethnic subcohort using discrimination, calibration, accuracy, and potential clinical usefulness. ResultsBoth models achieved excellent (Area under the curve (AUC) > 0.8) discrimination (AUCHGB = AUCRF = 0.86), calibration, and accuracy (HGB: slope = 1.00, intercept = -0.03, Brier score = 0.12; RF: slope = 0.97, intercept = 0.02, Brier score = 0.12) in the non-Hispanic White population (N = 224,073). Discrimination decreased in the White Hispanic (N = 10,429; AUC = 0.75 to 0.76), Black (N = 25,116; AUC = 0.77), Black Hispanic (N = 240; AUC = 0.78), Asian non-Hispanic (N = 4,809; AUC = 0.78 to 0.79), and overall (N = 267,194; AUC = 0.75 to 0.76) cohorts, but remained well-calibrated. We noted the poorest model discrimination (N = 1,870; AUC = 0.67 to 0.68) and calibration in the American-Indian cohort. ConclusionThe ML algorithms demonstrate an inferior predictive ability for 30-day complications following primary TJA in racial and ethnic minorities when trained on existing healthcare big data. This may be attributed to the disproportionate underrepresentation of minority groups within these databases, as demonstrated by the smaller sample sizes available to train the machine learning models. The ML models developed using smaller datasets (e.g., in racial and ethnic minorities) may not be as accurate as larger datasets, highlighting the need for equity-conscious model development.

Toward High-Quality Real-World Laboratory Data in the Era of Healthcare Big Data.

With Industry 4.0, big data and artificial intelligence have become paramount in the field of medicine. Electronic health records, the primary source of medical data, are not collected for research purposes but represent real-world data; therefore, they have various constraints. Although structured, laboratory data often contain unstandardized terminology or missing information. The major challenge lies in the lack of standardization of test results in terms of metrology, which complicates comparisons across laboratories. In this review, we delve into the essential components necessary for integrating real-world laboratory data into high-quality big data, including the standardization of terminology, data formats, equations, and the harmonization and standardization of results. Moreover, we address the transference and adjustment of laboratory results, along with the certification for quality of laboratory data. By discussing these critical aspects, we seek to shed light on the challenges and opportunities inherent to utilizing real-world laboratory data within the framework of healthcare big data and artificial intelligence.

Framework For Selecting Analytics Tools To Improve Health Care Big Data Usefulness in India

In the developing countries like India, there are certain challenges exist related to; how to build network between the patients, daignosis and medical personnel and including these how big data are being generated. The challenges include the relationship and interaction that exist between the big data within the various networks. Some of the challenges are like inaccuracy of data, inconsistency, and incompleteness of information and lack of confidence. The main problem arises is the inability to select the most appropriate analytics tools for the big data analysis in health sector. The objective of the current study was to provide best solution that can be used to solve the challenges of selecting analytical tools, to enhance big data interpretation which will improve the health care services in developing countries like India. For the study literarature of last 10 years of publication in the areas of big data, big data analytics of health care are collected. The data were analysed within the inerpretivist paradigm. From the analysis it was found that there are various factors which influence the big data and different tools were also analysed, the result formed a based on which a solution to solve dig data analytics problem. The solution contributes to information system and technologies in health sector. The conclusion of the study are; there exist a relationshoip between factors and the factors interaction in the process of providing medical services contribute to the souces of big data. Therefore, it is necessary to holistically analyse the bigdata from both technical and non-technical prespectives.

Big data applications: overview, challenges and future

Big Data (i.e., social big data, vehicular big data, healthcare big data etc) points to massive and complex data, that require special technologies and approaches for storage, processing, and analysis. Similarly, big data applications are software and systems utilizing large and complex datasets to extract insights, support decision-making, and address diverse business and societal challenges. Recently, the significance of big data applications has grown immensely for organizations across diverse sectors as they increasingly rely on insights derived from data. The increasing reliance on data insights has rendered traditional technologies and platforms inefficient due to scalability limitations and performance issues. This study contributes by identifying key domains impacted by big data, examining its effect on decision-making, addressing inherent complexities and opportunities, exploring core technologies, and offering solutions for potential concerns. Additionally, it conducts a comparative analysis to demonstrate the superiority of this research. These contributions provide valuable insights into the evolving landscape shaped by big data applications.

Retraction Note: Securing healthcare big data in industry 4.0: cryptography encryption with hybrid optimization algorithm for IoT applications

Retraction Note: Securing healthcare big data in industry 4.0: cryptography encryption with hybrid optimization algorithm for IoT applications

Blockchain-based privacy preservation framework for preventing cyberattacks in smart healthcare big data management systems

Blockchain-based privacy preservation framework for preventing cyberattacks in smart healthcare big data management systems

Factors influencing the use of big data within healthcare services: a systematic review.

Background: The emergence of big data holds the promise of aiding healthcare providers by identifying patterns and converting vast quantities of data into actionable insights facilitating the provision of precision medicine and decision-making. Objective: This study aimed to investigate the factors influencing use of big data within healthcare services to facilitate their use. Method: A systematic review was conducted in February 2024, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Database searches for articles published between 01 January 2020 and 18 February 2024 and included PubMed, Scopus, ProQuest and Cochrane Library. The Authority, Accuracy, Coverage, Objectivity, Date, Significance ( AACODS) checklist was used to evaluate the quality of the included articles. Subsequently, a thematic analysis was conducted on the findings of the review, using the Boyatzis approach. Results: A final selection of 46 studies were included in this systematic review. A significant proportion of these studies demonstrated acceptable quality, and the level of bias was deemed satisfactory. Thematic analysis identified seven major themes that influenced the use of big data in healthcare services. These themes were grouped into four primary categories: performance expectancy, effort expectancy, social influence, and facilitating conditions. Factors associated with "effort expectancy" were the most highly cited in the included studies (67%), while those related to "social influence" received the fewest citations (15%). Conclusion: This study underscored the critical role of "effort expectancy" factors, particularly those under the theme of "data complexity and management," in the process of using big data in healthcare services. Implications: Results of this study provide groundwork for future research to explore facilitators and barriers to using big data in health care, particularly in relation to data complexity and the efficient and effective management of big data, with significant implications for healthcare administrators and policymakers.

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.

JmBIG: enhancing dynamic risk prediction and personalized medicine through joint modeling of longitudinal and survival data in big routinely collected data

We have introduced the R package jmBIG to facilitate the analysis of large healthcare datasets and the development of predictive models. This package provides a comprehensive set of tools and functions specifically designed for the joint modelling of longitudinal and survival data in the context of big data analytics. The jmBIG package offers efficient and scalable implementations of joint modelling algorithms, allowing for integrating large-scale healthcare datasets.By utilizing the capabilities of jmBIG, researchers and analysts can effectively handle the challenges associated with big healthcare data, such as high dimensionality and complex relationships between multiple outcomes.With the support of jmBIG, analysts can seamlessly fit Bayesian joint models, generate predictions, and evaluate the performance of the models. The package incorporates cutting-edge methodologies and harnesses the computational capabilities of parallel computing to accelerate the analysis of large-scale healthcare datasets significantly. In summary, jmBIG empowers researchers to gain deeper insights into disease progression and treatment response, fostering evidence-based decision-making and paving the way for personalized healthcare interventions that can positively impact patient outcomes on a larger scale.

Oxidative balance scores in Korean adults are associated with periodontitis.

The Oxidative Balance Score (OBS) is a comprehensive metric that assesses the state of a person's oxidative balance. This study aimed to investigate the relationship between the oxidative balance score and moderate and severe periodontitis in a representative sample of Korean adults. Healthcare big data from the 7th Korea National Health and Nutrition Examination Survey (2016-2018) was used, and 16,489 adults aged ≥19years were included. Multivariate logistic regression analysis was performed to investigate the effect of sex-specific oxidative balance scores on periodontitis. In comparison with participants with a lower oxidative balance score, those with a higher oxidative balance score had a lower incidence of moderate and severe periodontitis (p<0.05). After adjusting for covariates, the oxidative balance score was negatively associated with moderate (odds ratio [OR]=0.952; 95% confidence interval [CI]: 0.934-0.971) and severe (OR=0.958; 95% CI: 0.931-0.986) periodontitis; however, the result was not significant for severe periodontitis in women's (OR=0.975; 95% CI: 0.934-1.018). Our study showed a statistically significant association between OBS and moderate and severe periodontitis, the small effect size should be interpreted with caution. The oxidative balance score was associated with moderate and severe periodontitis in Korean adults. Therefore, managing this score may help reduce the risk of periodontitis.

Reducing privacy risks of China's healthcare big data through the policy framework.

While healthcare big data brings great opportunities and convenience to the healthcare industry, it also inevitably raises the issue of privacy leakage. Nowadays, the whole world is facing the security threat of healthcare big data, for which a sound policy framework can help reduce privacy risks of healthcare big data. In recent years, the Chinese government and industry self-regulatory organizations have issued a series of policy documents to reduce privacy risks of healthcare big data. However, China's policy framework suffers from the drawbacks of the mismatched operational model, the inappropriate operational method, and the poorly actionable operational content. Based on the experiences of the European Union, Australia, the United States, and other extra-territorial regions, strategies are proposed for China to amend the operational model of the policy framework, improve the operational method of the policy framework, and enhance the operability of the operational content of the policy framework. This study enriches the research on China's policy framework to reduce privacy risks of healthcare big data and provides some inspiration for other countries.

Big data research in the business, management and accounting field: Revealing the thematic structure based on co-word analysis

Objective. This study aims to describe the thematic structure of big data research in business, management, and accounting. Design/Methodology/Approach. A co-word analysis was performed on 12,345 documents retrieved from Scopus from 2014-2023 in the category “business, management, and accounting”. Modularity algorithms were used to identify themes and subthemes, and the clustering of terms was qualitatively analyzed. Results/Discussion. Five main themes were identified: (1) Business and economic data analysis, (2) big data analytics in marketing, consumer behavior, and employee dynamics, (3) scalable machine learning and big data optimization, smart cities and urban development, (4) IoT-Driven innovations in industry 4.0 for optimized supply chain management, and (5) Social media and sentiment analysis in modern tourism and hospitality. The grouping of terms demonstrated the broad application of big data in healthcare, education, tourism, industry, organizational development, finance, social media, marketing, and hospitality. Conclusion. Big data is a field of application. This is evident in each cluster, where there are sub-themes that are nothing more than applying big data principles in sectors such as manufacturing, tourism services, education, health, and urbanization. Generally, the findings here are similar to other studies that have analyzed broader or more selective literature.

Data management in Nepalese medical sector: An empirical study

This study attempted to examine the influence of data management in the Nepalese medical sector and the impact of big data in health care. Many nations have strict policies and guidelines of data management of medical services. Countries with good ranking in health sector are the instances of this. Contemporary medical literatures have answered many issues, but the level of intensity or impact of unmanaged data in the medical sector and mechanisms to utilize EDI in the data management of the Nepalese medical sector are yet to be answered which is the quest of this article.