Big data in health architecture

The Internet of Things (IoT) in the healthcare system is rapidly changing from the conventional hospital and concentrated specialist behavior to a distributed, patient-centric approach. With the advancement of new techniques, a patient needs sophisticated healthcare requirements. IoT-enabled intelligent health monitoring system with sensors and devices is a patient analysis technique to monitor the patient 24 h a day. IoT is swapping the architecture and has improved the application of different complex systems...

BACKGROUND: Considering the soaring health-related costs directed toward a growing, aging, and comorbid population, the health sector needs effective data-driven interventions while managing rising care costs. While health interventions using data mining have become more robust and adopted, they often demand high-quality big data. However, growing privacy concerns have hindered large-scale data sharing. In parallel, recently introduced legal instruments require complex implementations, especially when it comes to biomedical data...

The Internet of things (IoT) is an emerging technology that enables ubiquitous devices to connect with the Internet. IoT technology has revolutionized the medical and healthcare industry by interconnecting smart devices and sensors. IoT-based devices and biosensors are ideal to detect diabetes disease by collecting the accurate value of glucose continuously. Diabetes is one of the well-known and major chronic diseases that has a worldwide social impact on community life. Blood glucose monitoring is a challenging task, and there is a need to propose a proper architecture of the noninvasive glucose sensing and monitoring mechanism, which could make diabetic people aware of self-management techniques...

This project aims to evaluate existing big data infrastructures for their applicability in the operating room to support medical staff with context-sensitive systems. Requirements for the system design were generated. The project compares different data mining technologies, interfaces, and software system infrastructures with a focus on their usefulness in the peri-operative setting. The lambda architecture was chosen for the proposed system design, which will provide data for both postoperative analysis and real-time support during surgery...

The planning of urban public health spatial can not only help people's physical and mental health but also help to optimize and protect the urban environment. It is of great significance to study the planning methods of urban public health spatial. The application effect of traditional urban public health spatial planning is poor, in this paper, urban public health spatial planning using big data technology and visual communication in the Internet of Things (IoT) is proposed. First, the urban public health spatial planning architecture is established in IoT, which is divided into the perception layer, the network layer and the application layer; Second, information collection is performed at the perception layer, and big data technology is used at the network layer to simplify spatial model information, automatically sort out spatial data, and establish a public health space evaluation system according to the type and characteristics of spatial data; Finally, the urban public health space is planned based on the health assessment results and the visual communication design concept through the application layer...

Atmospheric data are collected by researchers every day. Campaigns such as GOAmazon 2014/2015 and the Amazon Tall Tower Observatory collect essential data on aerosols, gases, cloud properties, and meteorological parameters in the Brazilian Amazon basin. These data products provide insights and essential information for analyzing and predicting natural processes. However, in Brazil, it is estimated that more than 80% of the scientific data collected are not published due to the lack of web portals that collect and store these data...

With the phenomenal rise in internet-of-things devices, the use of electroencephalogram (EEG) based brain-computer interfaces (BCIs) can empower individuals to control equipment with thoughts. These allow BCI to be used and pave the way for pro-active health management and the development of internet-of-medical-things architecture. However, EEG-based BCIs have low fidelity, high variance, and EEG signals are very noisy. These challenges compel researchers to design algorithms that can process big data in real-time while being robust to temporal variations and other variations in the data...

Lack of interoperability and integration between heterogeneous health systems is a big challenge to realize the potential benefits of eHealth. To best move from siloed applications to interoperable eHealth solutions, health information exchange (HIE) policy and standards are necessary to be established. However, there is no comprehensive evidence on the current status of HIE policy and standards on the African continent. Therefore, this paper aimed to systematically review the status of HIE policy and standards which are currently in practice in Africa...

OBJECTIVE: This study attempts to build a big data platform for disease burden that can realize the deep coupling of artificial intelligence and public health. This is a highly open and shared intelligent platform, including big data collection, analysis, and result visualization. METHODS: Based on data mining theory and technology, the current situation of multisource data on disease burden was analyzed. Putting forward the disease burden big data management model, functional modules, and technical framework, Kafka technology is used to optimize the transmission efficiency of the underlying data...

PURPOSE: In order to meet restrictions and difficulties in the development of hospital medical informatization and clinical databases in China, in this study, a disease-specific clinical database system (DSCDS) was designed and built. It provides support for the full utilization of real world medical big data in clinical research and medical services for specific diseases. METHODS: The development of DSCDS involved (1) requirements analysis on precision medicine, medical big data, and clinical research; (2) design schematics and basic architecture; (3) standard datasets of specific diseases consisting of common data elements (CDEs); (4) collection and aggregation of specific disease data scattered in various medical business systems of the hospital; (5) governance and quality improvement of specific disease data; (6) data storage and computing; and (7) design of data application modules...

The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies...

Historic blocks are valuable architectural and landscape heritage, and it is important to explore the distribution characteristics of tourists to historic blocks and their landscape preferences to realize the scientific construction and conservation of historic blocks and promote their sustainable development. At present, few studies combine the analysis of tourist distribution characteristics with landscape preferences. This study takes the historic block of Three Lanes and Seven Alleys in Fuzhou as an example, combines field research and questionnaires to construct a landscape preference evaluation indicator system for the historic block, measures the distribution characteristics of tourists in the block through the heat value of tourist flow obtained from the Tencent regional heat map, and analyses the influence of landscape preference indicators on the heat value of tourist flow in the block through stepwise multiple linear regression...

The management of soil organic carbon (SOC) stocks remains at the forefront of greenhouse gas mitigation. However, unprecedented anthropogenic disturbances emanating from continued land-use change have significantly altered SOC distribution across global biomes leading to considerable carbon losses. Consequently, understanding the spatial distribution of SOC across different biomes, particularly at larger scales, is critical for climate change policy formulation and planning. Advancements in remote sensing, availability of big data, and deep learning architecture offer great potential in large-scale SOC mapping...

BACKGROUND: Artificial intelligence technology has become a mainstream trend in the development of medical informatization. Because of the complex structure and a large amount of medical data generated in the current medical informatization process, big data technology to assist doctors in scientific research and analysis and obtain high-value information has become indispensable for medical and scientific research. METHODS: This study aims to discuss the architecture of diabetes intelligent digital platform by analyzing existing data mining methods and platform building experience in the medical field, using a large data platform building technology utilizing the Hadoop system, model prediction, and data processing analysis methods based on the principles of statistics and machine learning...

Implants are now the standard method of replacing missing or damaged teeth. Despite the improving technologies for the manufacture of implants and the introduction of new protocols for diagnosing, planning, and performing implant placement operations, the percentage of complications in the early postoperative period remains quite high. In this regard, there is a need to develop new methods for preliminary assessment of the patient's condition to predict the success of single implant survival. The intensive development of artificial intelligence technologies and the increase in the amount of digital information that is available for analysis make it relevant to develop systems based on neural networks for auxiliary diagnostics and forecasting...

Inertial sensor-based human activity recognition (HAR) has a range of healthcare applications as it can indicate the overall health status or functional capabilities of people with impaired mobility. Typically, artificial intelligence models achieve high recognition accuracies when trained with rich and diverse inertial datasets. However, obtaining such datasets may not be feasible in neurological populations due to, e.g., impaired patient mobility to perform many daily activities. This study proposes a novel framework to overcome the challenge of creating rich and diverse datasets for HAR in neurological populations...

Since the 1990s, Veterans Health Administration (VHA) has maintained a registry of Veterans with Spinal Cord Injuries and Disorders (SCI/Ds) to guide clinical care, policy, and research. Historically, methods for collecting and recording data for the VHA SCI/D Registry (VSR) have required significant time, cost, and staffing to maintain, were susceptible to missing data, and caused delays in aggregation and reporting. Each subsequent data collection method was aimed at improving these issues over the last several decades...

The ability to cross data from multiple sources represents a competitive advantage for organizations. Yet, the governance of the data lifecycle, from the data sources into valuable insights, is largely performed in an ad-hoc or manual manner. This is specifically concerning in scenarios where tens or hundreds of continuously evolving data sources produce semi-structured data. To overcome this challenge, we develop a framework for operationalizing and automating data governance. For the first, we propose a zoned data lake architecture and a set of data governance processes that allow the systematic ingestion, transformation and integration of data from heterogeneous sources, in order to make them readily available for business users...

During the COVID-19 pandemic, the patient care delivery paradigm rapidly shifted to remote technological solutions. Rising rates of life expectancy of older people, and deaths due to chronic diseases (CDs) such as cancer, diabetes and respiratory disease pose many challenges to healthcare. While the feasibility of Remote Patient Monitoring (RPM) with a Smart Healthcare Monitoring (SHM) framework was somewhat questionable before the COVID-19 pandemic, it is now a proven commodity and is on its way to becoming ubiquitous...

The advanced level of medical care is closely related to the development and popularity of a city, and it will also drive the development of tourism. The smart urban medical system based on big data analysis technology can greatly facilitate people's lives and increase the flow of people in the city, which is of great significance to the city's tourism image dissemination and branding. The medical system, with eight layers of architecture including access, medical cloud service governance, the medical cloud service resource, the platform's public service, the platform's runtime service, infrastructure, and the overall security and monitoring system of the platform, is designed based on big data analysis technology...