Application Of Big Data In Healthcare Research Articles

Unveiling the Landscape of Big Data Analytics in Healthcare: A Comprehensive Bibliometric Analysis

In the rapidly evolving landscape of healthcare, the digital transformation marked by healthcare 4.0 has spurred a surge in data generation, giving rise to ‘big data’. Big data analytics has become an effective tool in the healthcare industry, revolutionising medical research, patient care, and healthcare management. This study undertakes a meticulous bibliometric analysis, drawing upon a dataset of 2212 articles from the Scopus database spanning 2014 to 2023, to unravel the trajectory of big data analytics in healthcare. The research explores diverse dimensions, from the distribution of studies across years to the productivity rankings of journals, countries, and institutions, elucidating the evolving trends and key contributors. Co-authorship networks and keyword co-occurrence analysis reveal thematic clusters and intellectual structures, contributing to a nuanced understanding of the field. The results underscore the escalating global interest in the fusion of big data and healthcare, illuminating collaborations, and identifying influential players. Additionally, the study identifies pressing challenges, including security concerns and skill shortages, emphasizing the imperative of overcoming these barriers for effective big data applications in healthcare. Serving as a valuable resource for researchers, practitioners, and policymakers, this research not only captures the current landscape but also provides insights for future exploration, contributing to strategic planning in this dynamic domain.

Big Data Applications and Mining in the Healthcare Field

The healthcare sector faces unprecedented challenges due to global population growth, aging trends, and the continuous outbreak of diseases. This paper explores the significance and potential of big data applications in healthcare. We discuss challenges such as population aging, chronic disease management, and infectious disease transmission, highlighting big data's role in addressing these issues. We examine big data application mining methods, including data collection, storage, preprocessing, cleaning, and analysis, with applications in disease prediction, early diagnosis, clinical decision support, and epidemiological research, illustrated through case studies. Challenges encompass data privacy, ethics, data cleaning, integration, and model interpretability, necessitating continuous technological innovation. Future trends include enhanced data privacy, technological innovation, and interdisciplinary collaboration. Collaboration among research institutions, healthcare organizations, and government agencies is encouraged to advance big data application mining and contribute to healthcare progress. Overcoming challenges and embracing opportunities promises a healthier and more prosperous future.

Applying Apache Spark on Streaming Big Data for Health Status Prediction

Big data applications in healthcare have provided a variety of solutions to reduce costs, errors, and waste. This work aims to develop a real-time system based on big medical data processing in the cloud for the prediction of health issues. In the proposed scalable system, medical parameters are sent to Apache Spark to extract attributes from data and apply the proposed machine learning algorithm. In this way, healthcare risks can be predicted and sent as alerts and recommendations to users and healthcare providers. The proposed work also aims to provide an effective recommendation system by using streaming medical data, historical data on a user’s profile, and a knowledge database to make the most appropriate real-time recommendations and alerts based on the sensor’s measurements. This proposed scalable system works by tweeting the health status attributes of users. Their cloud profile receives the streaming healthcare data in real time by extracting the health attributes via a machine learning prediction algorithm to predict the users’ health status. Subsequently, their status can be sent on demand to healthcare providers. Therefore, machine learning algorithms can be applied to stream health care data from wearables and provide users with insights into their health status. These algorithms can help healthcare providers and individuals focus on health risks and health status changes and consequently improve the quality of life.

Application and Development Prospect of Artificial Intelligence and Big Data in Medical and Health Field

Artificial intelligence and big data technology have broad development prospects in the medical field. This article introduces the development of artificial intelligence and big data process, artificial intelligence and big data technology mainly in the field of medical health development condition, artificial intelligence and big data applications in healthcare, artificial intelligence and big data exist in the field of medical health challenges shows the four aspects of artificial intelligence and big data technology is widely used in the field of medical, think that artificial intelligence and big data promoting and deepening health reform, but also faces some obstacles in the development. In order to develop the supporting ability of intelligent medical treatment, some Suggestions are put forward, such as the combination of technology and products, demand, data exchange acquisition ability, interpretability of results and improvement of laws and regulations.

Prospects and Challenges of Population Health with Online and other Big Data in Africa: Understanding the Link to Improving Healthcare Service Delivery

Big data analytics offers promises to many health care service challenges and can provide answers to many population health issues. Big data is having a positive impact in almost every sphere of life in more advanced world while developing countries are striving to meet up. Even though healthcare systems in the developed world are recording some breakthroughs due to the application of big data, it is important to research the impact of big data in developing regions of the world, such as Africa and identify its peculiar needs. The purpose of this review was to summarize the challenges faced by big data analytics and the prospects that big data opens in health care services in Africa. The systematic review examined the key research questions to address whether big data applications can improve healthcare service delivery in Africa especially during epidemics or health crises and through the population health system. The paper examined prospects and challenges that are associated with the use of big data and healthcare service in relation to population health needs through influencing factors. In this study, literatures are reviewed to present cases of big data applications in healthcare in Africa and to understand the prospect and challenges of such applications to population health.

A review of big data applications of physiological signal data.

The proliferation of smart physiological signal monitoring sensors, combined with the advancement of telemetry and intelligent communication systems, has led to an explosion in healthcare data in the past few years. Additionally, access to cheaper and more effective power and storage mechanisms has significantly increased the availability of healthcare data for the development of big data applications. Big data applications in healthcare are concerned with the analysis of datasets which are too big, too fast, and too complex for healthcare providers to process and interpret with existing tools. The driver for the development of such systems is the continuing effort in making healthcare services more efficient and sustainable. In this paper, we provide a review of current big data applications which utilize physiological waveforms or derived measurements in order to provide medical decision support, often in real time, in the clinical and home environment. We focus mainly on systems developed for continuous patient monitoring in critical care and discuss the challenges that need to be overcome such that these systems can be incorporated into clinical practice. Once these challenges are overcome, big data systems have the potential to transform healthcare management in the hospital of the future.

Prospects of Big Data Analytics in Africa Healthcare System

Big data is having a positive impact in almost every sphere of life, such as in military intelligence, space science, aviation, banking, and health. Big data is a growing force in healthcare. Even though healthcare systems in the developed world are recording some breakthroughs due to the application of big data, it is important to research the impact of big data in developing regions of the world, such as Africa. Healthcare systems in Africa are, in relative terms, behind the rest of the world. Platforms and technologies used to amass big data such as the Internet and mobile phones are already in use in Africa, thereby making big data applications to be emerging. Hence, the key research question we address is whether big data applications can improve healthcare in Africa especially during epidemics and through the public health system. In this study, a literature review is carried out, firstly to present cases of big data applications in healthcare in Africa, and secondly, to explore potential ethical challenges of such applications. This review will provide an update on the application of big data in the health sector in Africa that can be useful for future researchers and health care practitioners in Africa.

Big Data in Healthcare:Applications and System Analytics

Starting with big data and big data in healthcare,firstly, challenges and improvements of big data in healthcare were elaborated. Then the background of big data and healthcare industry was presented. Finally, big data applications in healthcare were illustrated, and analysis on the systems of big data in healthcare and their key technologies were made.