Big Data In Healthcare Research Articles

Trivial State Fuzzy Processing for Error Reduction in Healthcare Big Data Analysis towards Precision Diagnosis.

There is a significant public health concern regarding medical diagnosis errors, which are a major cause of mortality. Identifying the root cause of these errors is challenging, and even if one is identified, implementing an effective treatment to prevent their recurrence is difficult. Optimization-based analysis in healthcare data management is a reliable method for improving diagnostic precision. Analyzing healthcare data requires pre-classification and the identification of precise information for precision-oriented outcomes. This article introduces a Cooperative-Trivial State Fuzzy Processing method for significant data analysis with possible derivatives. Trivial State Fuzzy Processing operates on the principle of fuzzy logic-based processing applied to structured healthcare data, focusing on mitigating errors and uncertainties inherent in the data. The derivatives are aided by identifying and grouping diagnosis-related and irrelevant data. The proposed method mitigates invertible derivative analysis issues in similar data grouping and irrelevance estimation. In the grouping and detection process, recent knowledge of the diagnosis progression is exploited to identify the functional data for analysis. Such analysis improves the impact of trivial diagnosis data compared to a voluminous diagnosis history. The cooperative derivative states under different data irrelevance factors reduce trivial state errors in healthcare big data analysis.

Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder.

Major Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies. This prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source "Pothos" air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels. Following initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness. This study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.

Leveraging Snomed Ct Within Standardized E-Clinical Pathways: A Key For Extensive Healthcare Big Data Analytics

Leveraging Snomed Ct Within Standardized E-Clinical Pathways: A Key For Extensive Healthcare Big Data Analytics

Regulating the Rational Use of Antimicrobial Drugs and Improving the Quality of Healthcare Big Data: A Development and Usability Study as A Prelude to AI Assisted Healthcare

Background: Healthcare big data has become an important strategic resource, and the study of applying it to the rational application of antimicrobial drugs is of great significance in improving and optimizing the various tasks of medicine and health and promoting the development of social health. Based on its own characteristics, the issue of data quality has important research value in promoting data output and application. Objective: To optimize the status quo of antimicrobial drug use in medical institutions, to explore the value of healthcare big data, and to explore the data governance methods in the era of digital intelligence in order to build a good ecosystem for the application of healthcare big data. Methods: This paper constructs a set of intelligent full-closed-loop antimicrobial rational application management platform integrated with data quality, adopts PDCA cycle management mode, analyzes the problems in the process of applying healthcare big data and gives the corresponding solutions, authorizes according to the personnel's duties, and guarantees the data security. Results: Since the platform went online in January 2021, the antimicrobial drug use intensity indicator of a hospital has decreased year by year from 38 to 34.4 as of June 2023, effectively standardizing the use of antimicrobial drugs. The time consumed for the statistics of antimicrobial use intensity related data has decreased from an average of 30min to 2min, which has significantly improved the work efficiency.Healthcare professionals are able to monitor indicator data in real time, with or without abnormal cases, and fully grasp the trend of indicators. At the same time, the patient's medication can be viewed at any time, and the condition can be grasped in a timely manner. Conclusions: The platform not only effectively solves the problem of information barriers, but also accurately determines the data source and statistical logic of indicator collection, reflecting the actual clinical diagnosis and treatment, and at the same time greatly reduces labor costs and optimizes the workflow.The practical experience of platform construction has important reference significance for the governance and application of healthcare big data, which provides data support and technical guarantee for the scientific, standardized and refined management of hospitals.

Modelling of healthcare data analytics using optimal machine learning model in big data environment

AbstractRecent advances in wireless networking, big data technologies, namely Internet of Things (IoT) 5G networks, health care big data analytics, and other technologies in artificial intelligence (AI) and wearables, have supported the progression of intellectual disease diagnosis methods. Medical data covers all patient data such as pharmacy texts, electronic health reports (EHR), prescriptions, study data from medical journals, clinical photographs, and diagnostic reports. Big data is a renowned method in the healthcare sector, with beneficial datasets that are highly difficult, voluminous, and rapid for healthcare providers for interpreting and computing using prevailing tools. This study combines concepts like deep learning (DL) and big data analytics in medical field. This article develops a new healthcare data analytics using optimal machine learning model in big data environment (HDAOML‐BDE) technique. The presented HDAOML‐BDE technique mainly aims to examine the healthcare data for disease detection and classification in the big data environment. For handling big data, the HDAOML‐BDE technique uses Hadoop MapReduce environment. In addition, the HDAOML‐BDE technique uses manta ray foraging optimization‐based feature selection (MRFO‐FS) technique to reduce high dimensionality problems. Moreover, the HDAOML‐BDE method uses relevance vector machine (RVM) model for the healthcare data environment. Furthermore, the arithmetic optimization algorithm (AOA) is utilized for the parameter tuning of the RVM classifier. The simulation results of the HDAOML‐BDE technique are tested on a healthcare dataset, and the outcomes portray the improved performance of the HDAOML‐BDE strategy over recent approaches in different measures.

Big data security and privacy in healthcare: A systematic review and future research directions

Big data security involves protecting healthcare data from unauthorised use and access and making any changes, while privacy entails ensuring the confidentiality of individual patient information within the datasets. The two are crucial in ensuring that big data is effectively applied for research and personalised care, compromising the sensitivity of healthcare data and medical information. Consequently, as the amount of data in the healthcare sector grows exponentially, securing sensitive data becomes increasingly crucial while effectively utilising big data analytics. The systematic review explores the issues and challenges associated with big data security and privacy in healthcare. Through reference to resource-based view theory, this paper seeks to examine the present state of research in this area, identify gaps in the existing literature, and propose strategies for future research. The search strategy looked at the data between 2013 and 2022 and yielded 93 studies from four databases: Scopus, PubMed, ScienceDirect and IEEE Xplore. From these studies, 18 papers are reviewed. The findings reveal that while significant progress has been made in securing healthcare data, numerous challenges still need to be considered. Overall, this review highlights the importance of continued research to ascertain that sensitive healthcare data is kept secure while allowing for the enhanced application of big data analytics. It contributes to the security and privacy problems associated with using big data in healthcare, measures for addressing these challenges, the healthcare value of big data, and foci for future research.

Legal Difficulties Associated With the Use of Big Data in Healthcare: Civil Law and Cyberlaw Review

Current trends suggest a future in which big data can be used to predict individual health risks and outcomes with exceptional accuracy. By analyzing large datasets that include various health indicators, algorithms can predict the onset of disease, allowing for early intervention and personalized treatment plans. However, the legal and ethical implications of predictive health analytics, such as potential misuse of predictive information or unfair discrimination based on health risks, require careful consideration. In terms of global health surveillance, the COVID-19 pandemic has highlighted the potential of big data to track the spread of disease and inform public health responses.

Privacy information protection of medical internet based on double chaos encryption algorithm

In order to avoid the risk of patients’ private information leakage, this paper puts forward a research on the protection of medical Internet private information based on double chaotic encryption algorithm. This paper analyzes the quantification of risk indicators for privacy information protection of medical Internet, establishes the risk quantification structure of health care big data according to the quantitative calculation results, and puts forward the strategy of controlling access to health care big data, configuring the risk level, describing the attributes of the system database, and realizing the privacy information protection of medical Internet under the double chaotic encryption algorithm. The experimental results show that the real identity of patients is protected to a certain extent in the protection of private information of medical internet after applying this method. Moreover, this method has high storage integrity and small storage standard deviation, and the method in this paper can effectively resist network intrusion. Therefore, it shows that this method has a good effect of protecting private information of medical Internet.

Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer

Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS’s ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today.

Enhancing Medical Data Analysis with Federated Learning in the Internet of Medical Things

The Internet of Things refers to physical items, which are equipped with software, sensors, computing power, and other technologies, and that communicate with other electronic devices and systems over communication networks or the Internet. A collection of medical devices and software programmes known as the Internet of Medical Things (IoMT) link to healthcare networks via internet computing. Machine-to-machine communication, which is the foundation of IoMT, is made feasible by medical equipment that includes Wi-Fi. IoMT devices have the ability to analyse and store collected data by connecting to cloud services. IoMT is a different moniker for IoT in healthcare. Since data is transferred via the internet and the IoMT creates a lot of data, privacy concerns are important. The vast volume of data produced by IoMT devices calls for big data processing, and federated learning tackles privacy issues as a way to overcome these difficulties. The big data health care framework for IoMT is discussed in this article. It is built on federated learning.

The role of big data in healthcare: A review of implications for patient outcomes and treatment personalization

The integration of big data analytics in healthcare has ushered in a transformative era, redefining the landscape of patient care and treatment strategies. This review examines the multifaceted implications of big data on patient outcomes and the individualization of medical interventions. Delving into the foundational elements of big data, we explore its evolution in the healthcare sector and highlight pivotal applications, such as predictive analytics, treatment personalization, and population health management. The paper underscores how big data-driven insights have revolutionized diagnosis and early detection, leading to more accurate and timely interventions. Treatment planning has witnessed a paradigm shift, with the tailoring of therapeutic approaches based on robust data analyses, fostering the realization of personalized medicine. Moreover, the role of big data in enhancing patient engagement and empowerment is explored, illuminating the potential for collaborative and informed decision-making. Despite these advancements, ethical considerations and challenges loom large. Privacy concerns, data security, and the ethical use of patient information demand meticulous attention to ensure the responsible application of big data in healthcare. The paper discusses the evolving regulatory frameworks and strategies to address these pressing issues. Looking ahead, the review outlines emerging trends and technologies poised to shape the future of big data in healthcare. It identifies research opportunities and encourages interdisciplinary collaborations to further propel innovation in this dynamic field. By addressing challenges and envisioning future possibilities, it seeks to contribute to the ongoing dialogue surrounding the responsible and impactful integration of big data in shaping the future of healthcare.

The Transformative Impact of Big Data in Healthcare: Improving Outcomes, Safety, and Efficiencies

The integration of Big Data analytics into the healthcare sector has initiated a period of unparalleled transformation, significantly influencing patient care, research, and healthcare administration. This detailed synthesis, derived from a systematic literature review of 31 articles published between 2013 and 2023, examines the extensive impact of Big Data within the healthcare industry. It addresses how Big Data enhances personalized medicine and optimizes treatment protocols, promotes the use of predictive analytics for the early detection and management of diseases, and bolsters patient safety through the implementation of real-time monitoring and decision support systems. The review also confronts the challenges and limitations associated with Big Data's application, including concerns over data privacy and security, ethical dilemmas, interoperability issues, and the necessity for both skilled personnel and advanced technological infrastructure. Furthermore, this overview ventures into future prospects and technological advancements, emphasizing the pivotal role of emerging technologies such as artificial intelligence (AI), machine learning, and blockchain in heralding a new era of healthcare. It highlights Big Data's instrumental role in supporting public health measures and preparing for pandemics, alongside forecasting the ongoing influence of Big Data in fostering innovative healthcare paradigms. This analysis advocates for a concerted effort among healthcare professionals, policymakers, and technology experts to fully leverage Big Data's capabilities in healthcare, aiming to enhance health outcomes, increase operational efficiencies, and ensure the long-term viability of healthcare systems.

보건의료 빅데이터 정책과 정치 갈등

In relation to healthcare big data policy, various issues and conflicts are arising from the perspective of healthcare information protection and use. In particular, conflicts among political actors intensified as issues related to healthcare big data policy were not resolved even during the National Assembly review process. Therefore, the purpose of this article is to reveal factors causing conflict and consider improvement plans through analysis of the National Assembly legislative process and of the conflict factors in actors related to healthcare big data policy. To this end, we analyze the case of amendments to the Insurance Business Act related to the computerization of medical indemnity insurance claims, which can be defined as a recently enacted healthcare big data policy. As a result of the analysis, issues include the accumulation of sensitive personal medical information by private insurance companies, the use of the accumulated big data for purposes other than the purpose, the problem of medical institutions providing personal medical information such as medical records to third parties, the problem of system legitimacy of the Medical Service Act, Pharmaceutical Affairs Act, and Insurance Business Act, the problem of the patient’s right to request transmission of data other than own, and the problem of the right to self-determination regarding the scope and method of transmission of data were presented. This became a factor in causing conflict, with the government and insurance industry taking a pro stance and the medical community and patient groups taking a strong opposing stance. To resolve issues and conflicts, It is necessary to implement improvement measures such as establishing a governance structure to resolve system legitimacy between laws between standing committees during the review process of the National Assembly, setting patient-centered policy directions, strengthening selfdetermination and control rights over healthcare big data, and setting policy directions that prioritize the protection of healthcare big data.

A Survey on the Utilization of Big Data in the Healthcare Sector and the Demand for Integrated Data

Objectives: To investigate the status of secondary data and big data platform utilization and the demand for integrated data in the health care sector.Methods: An online survey was conducted using a structured questionnaire targeting members of six academic societies in the healthcare field. A total of 220 researchers responded to the survey.Results: 88.2% of respondents said they had experience using secondary data sources in the healthcare field, and 19.0% responded that they had experience linking data using the healthcare big data platform. Among the data in the health and medical field, the most used dataset was health insurance claims data, and if linking between dataset, the most desired linked data type is ‘National Health and Nutrition Examination Survey-Health Insurance Claims Data–National Health Examination-Death Information’. The advantages of linked data on big data platforms were ‘large volume of big data or representative’, ‘diversity of data’, and ‘long-term follow-up’. The major items requiring improvement were ‘delay in time to provide data’, ‘limitation of data provision range’, and ‘low linkage rate between dataset’.Conclusions: To promote the use of linked data in the health care field, comprehensive efforts are needed, including rapid data provision, improvement of the combination rate, standardization of data, and establishment of ethical guidelines.

A Brief Review of Big Data in Healthcare: Challenges and Issues, Recent Developments, and Future Directions

Big data is improving the healthcare industry and creating opportunities for improved patient care, personalized medicine, and advanced research. This brief review article aims to survey the challenges and issues connected with big data in healthcare, discuss recent developments in the field, and highlight future directions for helping big data to enhance healthcare outcomes by understanding these aspects, participants can make informed decisions and contribute to the advancement of big data analytics in healthcare.

Assessing heatwave effects on disabled persons in South Korea.

This study investigated the risk of heatwaves for people with disabilities and other socioeconomic attributes using Health Care Bigdata in South Korea. The Health Care Bigdata provides detailed information on heat-related illness (HRI) patients in 2011-2020 from seven major cities. We employed the Distributed Lag Nonlinear Model (DLNM) to measure heat waves' relative risk. Our findings are four-fold. First, the relative risk (RR) of disabled people was 5.075 (95% confidence interval 4.476-5.674), significantly surpassing that of non-disabled people, 3.296 (2.517-4.075). Second, among various personal characteristics studied, disability influenced RR the most, exceeding impacts from elderly (4.457: 3.748-5.166), low-income (3.909: 3.004-4.813), and outdoor (4.052: 2.940-5.164). Third, the disabled young group (5.305: 4.414-6.195) was more vulnerable than the non-disabled elderly group (4.287: 3.576-4.999). Lastly, no significant difference in relative risk was observed between the mild (4.413: 3.855-4.971) and severe disabled groups (4.013: 3.121-4.905).

Temporal-Spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit.

Recent advancements in medical information technology have enabled electronic health records (EHRs) to store comprehensive clinical data which has ushered healthcare into the era of "big data". However, medical data are rather complicated, making problem-solving in healthcare be limited in scope and comprehensiveness. The rapid development of deep learning in recent years has opened up opportunities for leveraging big data in healthcare. In this article we introduce a temporal-spatial correlation attention network (TSCAN) to address various clinical characteristic prediction problems, including mortality prediction, length of stay prediction, physiologic decline detection, and phenotype classification. Leveraging the attention mechanism model's design, our approach efficiently identifies relevant items in clinical data and temporally correlated nodes based on specific tasks, resulting in improved prediction accuracy. Additionally, our method identifies crucial clinical indicators associated with significant outcomes, which can inform and enhance treatment options. Our experiments utilize data from the publicly accessible Medical Information Mart for Intensive Care (MIMIC-IV) database. Finally, our approach demonstrates notable performance improvements of 2.0% (metric) compared to other SOTA prediction methods. Specifically, we achieved an impressive 90.7% mortality rate prediction accuracy and 45.1% accuracy in length of stay prediction.

Clinical big data in healthcare: A survey of medical computers

Big data is a massive amount of information, measurements, and observations, where it has the power to provide a solution to the impossibilities. Recently, it has become the most trending topic in the field of data analysis because of its amazing potentials in extracting the hidden facts. Which attracted various sectors all over the world to collect and analyze the big data in order to improve their services and introduce high valuable products. Specifically, in the healthcare industry, different sources generate big data such as; hospital records, medical records of patients, and results of medical examinations. This type of data is related to the population healthcare, and it requires analysis in order to extract valuable knowledge. Nowadays, with the available high-end computing solutions for big data analysis. It becomes easy for researchers to have solutions that improve the healthcare level of the population. The promising thinking to give new technologies, high services, and big profits for healthcare, can revolutionize the medical solutions and help the community in overcoming the impossible cases. This research discusses essential clinical big data matters related to the healthcare sector by introducing a clear definition and features of the clinical big data in healthcare and its process. Also, by presenting analytics, applications, benefits, challenges, and future of the clinical big data technologies in the healthcare sector. This survey aims to review state of the art for the application of the clinical big data in the healthcare sector, in which it would be an apparent reference, where authors can refer to in their future research.

Long-term toxicities after allogeneic hematopoietic stem cell transplantation with or without total body irradiation: a population-based study in Korea.

To compare long-term toxicity incidences, including secondary cancer (SC) with or without total body irradiation (TBI), in Asian patients receiving allogeneic hematopoietic stem cell transplantation (HSCT) using a nationwide database. We identified 4,554 patients receiving HSCT for leukemic disease from 2009 to 2016 using the healthcare bigdata system of Korea. Incidence rate ratios (IRRs) for SC, cataracts, hypothyroidism, chronic kidney disease (CKD), myocardial infarction, or strokes were compared, and standardized incidence ratios (SIR) of SC was also estimated. TBI was conducted on 1,409 patients (30.9%). No overall survival differences based on TBI were observed. With a median follow-up duration of 58.2 months, 143 patients were diagnosed with subsequent SC (3.4%). Incidence rates per 1,000 person-year were 6.56 (95% confidence interval [CI], 4.8-8.8) and 7.23 (95% CI, 5.9-8.8) in the TBI and no-TBI groups, respectively (p = 0.594). Also, the SIR (95% CI) was not significantly increased by TBI (1.32 [0.86-1.94] vs. 1.39 [1.08-1.77] in the no-TBI group). In the young age group (0-19 years), SIRs were increased in both groups regardless of TBI (8.60 vs. 11.96). The IRRs of cataracts (1.60; 95% CI, 1.3-2.0), CKD (1.85; 95% CI, 1.3-2.6), and hypothyroidism (1.50; 95% CI, 1.1-2.1) were significantly increased after TBI. However, there were no significant differences in the occurrence of myocardial infarction and stroke according to TBI. Our results suggest that modern TBI may not additionally increase the risk of SC after allogeneic HSCT, although increased risks of other diseases were noted. Physicians should carefully consider individualized risks and benefits of TBI, with a particular focus by age group.

Big Data Security on Hadoop Open Source Frame for Healthcare Data Management using One-Time-Pad Encryption Algorithm

The study elicited knowledge about the factors associated with one-time pad encryption/decryption with big data in healthcare; formulate an assembled algorithms model for one-time pad encryption; design and implement the system and evaluating the system performance with the view implementing big data security on Hadoop open-source framework for healthcare data. Literature was sourced to investigate the factors associated with healthcare security attacks and various consequences of breach of data. An assembled algorithm model was formulated using mathematical theory of one-time pad encryption and a model was designed using Universal Modelling Language (UML) and implemented using python programming language, Distributed File System of Hadoop, Yet Another Resource Negotiator called YARN; encryption time and decryption time was adopted for the performance metrics deployed for the evaluation of the developed system. The result showed that as the size of the files increased, the encryption/decryption time keeps increasing as well. While carryout the algorithm evaluation, two different values (file sizes) were used for testing on the Hadoop framework.Securing the healthcare (Ebola) big-data, it was observed that OTP encryption/decryption performed better compared to AES encryption/decryption in term of computational processing time of the healthcare big-data considered. Considering before/after downloading, it was observed that there was need for authentication for another level of security towards securing healthcare records on HDFS. The study concluded that, big data analytics on Hadoop is ideal for today’s big healthcare data and also that One Time Pad encryption algorithm is sufficient to provide needed big healthcare data security.