Relevant bibliographies by topics / Big data in healthcare

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Big data in healthcare'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Big data in healthcare"

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Viceconti, Marco, Peter Hunter, and Rod Hose. "Big Data, Big Knowledge: Big Data for Personalized Healthcare." IEEE Journal of Biomedical and Health Informatics 19, no. 4 (July 2015): 1209–15. http://dx.doi.org/10.1109/jbhi.2015.2406883.

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Sarkar, Bikash Kanti. "Big Data and Healthcare Data." International Journal of Knowledge-Based Organizations 7, no. 4 (October 2017): 50–77. http://dx.doi.org/10.4018/ijkbo.2017100104.

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Big data and its analytics yield a lot of opportunities to make great progresses in many fields, ranging from economic and business activities to public administration, from national security to scientific researches and so on. However, the most noticeable point is that healthcare data has been recently identified as a prime example of big data. Undoubtedly, efficient use of healthcare resources has become a key factor in improving overall healthcare system. But for managing healthcare data and obtaining potential results, we need integration and sharing of data that ultimately demand the concept of distributed system. The paper in its first phase gives an overview on big data and healthcare data from different aspects. A review on the state-of-the-art distributed file system (Hadoop) is conducted in this stage too. The primary aim of this phase is to provide an overall picture on big data as well as healthcare data for non-expert readers. In the next phase, a cloud-based e-health system is proposed for the expert audiences. The expected promising characteristics as well as the managerial implications of the model are highlighted in the analysis section.
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Sadiku, Matthew N. O. "Big Data in Healthcare." International Journal for Research in Applied Science and Engineering Technology 7, no. 9 (September 30, 2019): 1165–68. http://dx.doi.org/10.22214/ijraset.2019.9167.

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Shukla, Purvika. "Big Data: Healthcare Informatics." International Journal for Research in Applied Science and Engineering Technology V, no. XI (November 20, 2017): 1147–59. http://dx.doi.org/10.22214/ijraset.2017.11170.

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Pramanik, Pijush Kanti Dutta, Saurabh Pal, and Moutan Mukhopadhyay. "Big Data and Big Data Analytics for Improved Healthcare Service and Management." International Journal of Privacy and Health Information Management 8, no. 1 (January 2020): 13–51. http://dx.doi.org/10.4018/ijphim.2020010102.

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Like other fields, the healthcare sector has also been greatly impacted by big data. A huge volume of healthcare data and other related data are being continually generated from diverse sources. Tapping and analysing these data, suitably, would open up new avenues and opportunities for healthcare services. In view of that, this paper aims to present a systematic overview of big data and big data analytics, applicable to modern-day healthcare. Acknowledging the massive upsurge in healthcare data generation, various ‘V's, specific to healthcare big data, are identified. Different types of data analytics, applicable to healthcare, are discussed. Along with presenting the technological backbone of healthcare big data and analytics, the advantages and challenges of healthcare big data are meticulously explained. A brief report on the present and future market of healthcare big data and analytics is also presented. Besides, several applications and use cases are discussed with sufficient details.
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Kunnavil, Radhika. "Healthcare Data Utilization for the Betterment of Mankind - An Overview of Big Data Concept in Healthcare." International Journal of Healthcare Education & Medical Informatics 05, no. 02 (August 24, 2018): 14–17. http://dx.doi.org/10.24321/2455.9199.201807.

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Kim, Ha-Na. "Use of Healthcare Big Data." Korean Journal of Family Practice 7, no. 3 (June 20, 2017): 307. http://dx.doi.org/10.21215/kjfp.2017.7.3.307.

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Ryu, Seewon, and Tae-Min Song. "Big Data Analysis in Healthcare." Healthcare Informatics Research 20, no. 4 (2014): 247. http://dx.doi.org/10.4258/hir.2014.20.4.247.

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Asri, Hiba, Hajar Mousannif, and Hassan Al Moatassime. "Big Data Analytics in Healthcare." International Journal of Distributed Systems and Technologies 10, no. 4 (October 2019): 45–58. http://dx.doi.org/10.4018/ijdst.2019100104.

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Sensors and mobile phones shine in the Big Data area due to their capabilities to retrieve a huge amount of real-time data; which was not possible previously. In the specific field of healthcare, we can now collect data related to human behavior and lifestyle for better understanding. This pushed us to benefit from such technologies for early miscarriage prediction. This research study proposes to combine the use of Big Data analytics and data mining models applied to smartphones real-time generated data. A K-means data mining algorithm is used for clustering the dataset and results are transmitted to pregnant woman to make quick decisions; with the intervention of her doctor; through an android mobile application that we created. As well, she receives recommendations based on her behavior. We used real-world data to validate the system and assess its performance and effectiveness. Experiments were made using the Big Data Platform Databricks.
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Belle, Ashwin, Raghuram Thiagarajan, S. M. Reza Soroushmehr, Fatemeh Navidi, Daniel A. Beard, and Kayvan Najarian. "Big Data Analytics in Healthcare." BioMed Research International 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/370194.

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The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.
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Dissertations / Theses on the topic "Big data in healthcare"

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Castaño, Martínez María, and Elizabeth Johnson. "Communicating big data in the healthcare industry." Thesis, Linnéuniversitetet, Institutionen för marknadsföring (MF), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-96251.

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In recent years nearly every aspect of how we function as a society has transformed from analogue to digital. This has spurred extraordinary change and acted as a catalyst for technology innovation, as well as big data generation. Big data is characterized by its constantly growing volume, wide variety, high velocity, and powerful veracity. With the emergence of COVID-19, the global pandemic has demonstrated the profound impact, and often dangerous consequences, when communicating health information derived from data. Healthcare companies have access to enormous data assets, yet communicating information from their data sources is complex as they also operate in one of the most highly regulated business environments where data privacy and legal requirements vary significantly from one country to another. The purpose of this study is to understand how global healthcare companies communicate information derived from data to their internal and external audiences. The research proposes a model for how marketing communications, public relations, and internal communications practitioners can address the challenges of utilizing data in communications in order to advance organizational priorities and achieve business goals. The conceptual framework is based on a closed-loop communication flow and includes an encoding process specialized for incorporating big data into communications. The results of the findings reveal tactical communication strategies, as well as organizational and managerial practices that can position practitioners best for communicating big data. The study concludes by proposing recommendations for future research, particularly from interdisciplinary scholars, to address the research gaps.
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Mohamed, M. (Mahmoud). "Platforms for big data business models in the healthcare context." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201906052419.

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Abstract. The profitability of the business opportunity is defined by the level of owned data and its insights to the business organization. However, the existing literature has not identified how to link between different business models in the data-oriented systems. The previous research efforts focused on the technical aspects of data including data monetization, clustering, and data lifecycle. The purpose of this research is to understand how to link big data and business model thinking in the healthcare context. The main argument of this study provides a novel way to the modularity in the big data business models, which enables the system customers to control the system Studies show if there is a kind of data-oriented platform that remind patients to do certain tasks (ex. nutrition and medicine reminders) before going to doctors and nurses; the patients would like to use it. In addition, around 90% of the platform users will recommend it to other patients and so on. This pushes the operators in the healthcare industry to transform their traditional human-based data systems into a computer-to-computer system. In the data-intensive systems like the healthcare industry, the value creation is done by monetizing data between system actors to analyze the data and develop extensive knowledge about the end customer. For example, the hospitals have the right to own and anonymize the patient data to ensure the privacy and security of patient information. Then hospitals monetize the patient data with their business partner who has the technical and analytical capability to analyze data. Later, they provide the system with useful insights gained from data analytics. This is an exploratory phase of research where the qualitative case study approach is applied to examine the possibility of having a common platform for the integrated solutions in the data-oriented systems. To approach these platforms, an empirical study has been conducted over three case companies working in the healthcare context. The data were collected using semi-structured interview discussion. Similar qualitative approaches have been used in some prior studies to examine the value creation in the data-oriented systems and identify the future business models for the digital environments and IoT. This research contributes to the existing literature by identifying four main platforms for big data business models. The modular platform is done due to the lack of knowledge about the end-customer, it grants system partners the right to control over their platforms. The partnership platform guarantees the continuity of the business process, the Ecosystemic platform gives the end customer the possibility to select what they need from the overall ecosystem. The ownership platform is related to the centralized control over the data source, enabling consistency of the business process.
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Pšurný, Michal. "Big data analýzy a statistické zpracování metadat v archivu obrazové zdravotnické dokumentace." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316821.

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This Diploma thesis describes issues of big data in healthcare focus on picture archiving and communication system. DICOM format are store images with header where it could be other valuable information. This thesis mapping data from 1215 studies.
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Mgudlwa, Sibulela. "A big data analytics framework to improve healthcare service delivery in South Africa." Thesis, Cape Peninsula University of Technology, 2018. http://hdl.handle.net/20.500.11838/2877.

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2018.
Healthcare facilities in South Africa accumulate big data, daily. However, this data is not being utilised to its full potential. The healthcare sector still uses traditional methods to store, process, and analyse data. Currently, there are no big data analytics tools being used in the South African healthcare environment. This study was conducted to establish what factors hinder the effective use of big data in the South African healthcare environment. To fulfil the objectives of this research, qualitative methods were followed. Using the case study method, two healthcare organisations were selected as cases. This enabled the researcher to find similarities between the cases which drove them towards generalisation. The data collected in this study was analysed using the Actor-Network Theory (ANT). Through the application of ANT, the researcher was able to uncover the influencing factors behind big data analytics in the healthcare environment. ANT was essential to the study as it brought out the different interactions that take place between human and non-human actors, resulting in big data. From the analysis, findings were drawn and interpreted. The interpretation of findings led to the developed framework in Figure 5.5. This framework was developed to guide the healthcare sector of South Africa towards the selection of appropriate big data analytics tools. The contribution of this study is in twofold; namely, theoretically and practically. Theoretically, the developed framework will act as a useful guide towards the selection of big data analytics tools. Practically, this guide can be used by South African healthcare practitioners to gain better understanding of big data analytics and how they can be used to improve healthcare service delivery.
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Nováková, Martina. "Analýza Big Data v oblasti zdravotnictví." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-201737.

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This thesis deals with the analysis of Big Data in healthcare. The aim is to define the term Big Data, to acquaint the reader with data growth in the world and in the health sector. Another objective is to explain the concept of a data expert and to define team members of the data experts team. In following chapters phases of the Big Data analysis according to methodology of EMC2 company are defined and basic technologies for analysing Big Data are described. As beneficial and interesting I consider the part dealing with definition of tasks in which Big Data technologies are already used in healthcare. In the practical part I perform the Big Data analysis task focusing on meteorotropic diseases in which I use real medical and meteorological data. The reader is not only acquainted with the one of recommended methods of analysis and with used statistical models, but also with terms from the field of biometeorology and healthcare. An integral part of the analysis is also information about its limitations, the consultation on results, and conclusions of experts in meteorology and healthcare.
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Jonsson, Hanna, and Luyolo Mazomba. "Revenue Generation in Data-driven Healthcare : An exploratory study of how big data solutions can be integrated into the Swedish healthcare system." Thesis, Umeå universitet, Företagsekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-161384.

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Abstract The purpose of this study is to investigate how big data solutions in the Swedish healthcare system can generate a revenue. As technology continues to evolve, the use of big data is beginning to transform processes in many different industries, making them more efficient and effective. The opportunities presented by big data have been researched to a large extent in commercial fields, however, research in the use of big data in healthcare is scarce and this is particularly true in the case of Sweden. Furthermore, there is a lack in research that explores the interface between big data, healthcare and revenue models. The interface between these three fields of research is important as innovation and the integration of big data in healthcare could be affected by the ability of companies to generate a revenue from developing such innovations or solutions. Thus, this thesis aims to fill this gap in research and contribute to the limited body of knowledge that exists on this topic. The study conducted in this thesis was done via qualitative methods, in which a literature search was done and interviews were conducted with individuals who hold managerial positions at Region Västerbotten. The purpose of conducting these interviews was to establish a better understanding of the Swedish healthcare system and how its structure has influenced the use, or lack thereof, of big data in the healthcare delivery process, as well as, how this structure enables the generation of revenue through big data solutions. The data collected was analysed using the grounded theory approach which includes the coding and thematising of the empirical data in order to identify the key areas of discussion. The findings revealed that the current state of the Swedish healthcare system does not present an environment in which big data solutions that have been developed for the system can thrive and generate a revenue. However, if action is taken to make some changes to the current state of the system, then revenue generation may be possible in the future. The findings from the data also identified key barriers that need to be overcome in order to increase the integration of big data into the healthcare system. These barriers included the (i) lack of big data knowledge and expertise, (ii) data protection regulations, (iii) national budget allocation and the (iv) lack of structured data. Through collaborative work between actors in both the public and private sectors, these barriers can be overcome and Sweden could be on its way to transforming its healthcare system with the use of big data solutions, thus, improving the quality of care provided to its citizens. Key words: big data, healthcare, Swedish healthcare system, AI, revenue models, data-driven revenue models
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Jamthe, Anagha. "Mitigating interference in Wireless Body Area Networks and harnessing big data for healthcare." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341798.

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Saenyi, Betty. "Opportunities and challenges of Big Data Analytics in healthcare : An exploratory study on the adoption of big data analytics in the Management of Sickle Cell Anaemia." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Informatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-42864.

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Background: With increasing technological advancements, healthcare providers are adopting electronic health records (EHRs) and new health information technology systems. Consequently, data from these systems is accumulating at a faster rate creating a need for more robust ways of capturing, storing and processing the data. Big data analytics is used in extracting insight form such large amounts of medical data and is increasingly becoming a valuable practice for healthcare organisations. Could these strategies be applied in disease management? Especially in rare conditions like Sickle Cell Disease (SCD)? The study answers the following research questions;1. What Data Management practices are used in Sickle Cell Anaemia management?2. What areas in the management of sickle cell anaemia could benefit from use of big data Analytics?3. What are the challenges of applying big data analytics in the management of sickle cell anaemia?Purpose: The purpose of this research was to serve as pre-study in establishing the opportunities and challenges of applying big data analytics in the management of SCDMethod: The study adopted both deductive and inductive approaches. Data was collected through interviews based on a framework which was modified specifically for this study. It was then inductively analysed to answer the research questions.Conclusion: Although there is a lot of potential for big data analytics in SCD in areas like population health management, evidence-based medicine and personalised care, its adoption is not a surety. This is because of lack of interoperability between the existing systems and strenuous legal compliant processes in data acquisition.
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Wang, Mengyuan. "The way of chinese medical reform : new trends in the era of the “internet+” and big data." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/18585.

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Mestrado em Desenvolvimento e Cooperação Internacional
A China é um país com uma população imensa, com recursos médicos insuficientes e distribuição desigual. Portanto, existem muitos problemas no serviço de saúde. Devido ao desenvolvimento atrasado do sistema médico, a qualidade dos recursos médicos é baixa, o custo é alto e a eficiência dos serviços médicos é baixa. Um dos principais fatores explicativos dessa situação é a falta de apoio do governo e seguro médico imperfeito. Para resolver esse problema, o governo começou a reformar o sistema de segurança médica. Desde a reforma do seguro médico de 1988, após várias mudanças, o sistema de seguro médico da China amadureceu gradualmente. A tese descreve brevemente a estrutura básica, o conteúdo e o caminho da mudança nos cuidados de saúde. E as deficiências do atual sistema de seguro médico. A análise introduz o papel da "Internet+" e da "big data" na reforma do sistema de seguro médico e avalia as potencialidades da sua introdução e operacionalização para a gestão e governança do sistema de saúde.
China is a population republic country has insufficient medical resources and uneven distribution. Therefore, there are many medical problems. Due to the backward development of the medical system, the quality of medical resources is poor, the efficiency of medical services is low, and the cost is high, which brings many difficulties for the Chinese people to seek medical treatment. However, one of the main factors of these problems is the lack of government support and imperfect medical insurance. To solve this problem, the government began to reform the medical security system. Since the 1988 medical insurance reform, after several changes, China's medical insurance system has gradually matured. The thesis will briefly describe the basic framework, content and path of change in health care. And the shortcomings of the current medical insurance system. According to the characteristics of the times, talk about the impact of "Internet +" and "Big Data" on the current Chinese industry, including people's lives. Therefore, the analysis introduces the positive role of big data Internet for the reform of medical insurance system, and provides convenience for the management and governance of medical insurance system. Analyze whether "Internet +" and "Big Data" can lead to new trends in the reform of the health care system.
info:eu-repo/semantics/publishedVersion
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Ramadoss, Balaji. "Ontology Driven Model for an Engineered Agile Healthcare System." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5110.

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Healthcare is in urgent need of an effective way to manage the complexity it of its systems and to prepare quickly for immense changes in the economics of healthcare delivery and reimbursement. Centers for Medicare & Medicaid Services (CMS) releases policies affecting inpatient and long-term care hospitals policies that directly affect reimbursement and payment rates. One of these policy changes, a quality-reporting program called Hospital Inpatient Quality Reporting (IQR), will effect approximately 3,400 acute-care and 440 long-term care hospitals. IQR sets guidelines and measures that will contain financial incentives and penalties based on the quality of care provided. CMS, the largest healthcare payer, is aggressively promoting high quality of care by linking payment incentives to outcomes. With CMS assessing each hospital's performance by comparing its Quality Achievements and Quality Improvement scores, there is a growing need and demand to understand these quality measures under the context of patient care, data management and system integration. This focus on patient-centered quality care is difficult for healthcare systems due to the lack of a systemic view of the patient and patient care. This research uniquely addresses the hospital's need to meet these challenges by presenting a healthcare specific framework and methodology for translating data on quality metrics into actionable processes and feedback to produce the desired quality outcome. The solution is based on a patient-care level process ontology, rather than the technology itself, and creates a bridge that applies systems engineering principles to permit observation and control of the system. This is a transformative framework conceived to meet the needs of the rapidly changing healthcare landscape. Without this framework, healthcare is dealing with outcomes that are six to seven months old, meaning patients may not have been cared for effectively. In this research a framework and methodology called the Healthcare Ontology Based Systems Engineering Model (HOB-SEM) is developed to allow for observability and controllability of compartmental healthcare systems. HOB-SEM applies systems and controls engineering principles to healthcare using ontology as the method and the data lifecycle as the framework. The ontology view of patient-level system interaction and the framework to deliver data management and quality lifecycles enables the development of an agile systemic healthcare view for observability and controllability
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Books on the topic "Big data in healthcare"

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Amirian, Pouria, Trudie Lang, and Francois van Loggerenberg, eds. Big Data in Healthcare. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62990-2.

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Ebeling, Mary F. E. Healthcare and Big Data. New York: Palgrave Macmillan US, 2016. http://dx.doi.org/10.1057/978-1-137-50221-6.

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Kulkarni, Anand J., Patrick Siarry, Pramod Kumar Singh, Ajith Abraham, Mengjie Zhang, Albert Zomaya, and Fazle Baki, eds. Big Data Analytics in Healthcare. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31672-3.

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Shen, Bairong, ed. Healthcare and Big Data Management. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6041-0.

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Househ, Mowafa, Andre W. Kushniruk, and Elizabeth M. Borycki, eds. Big Data, Big Challenges: A Healthcare Perspective. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06109-8.

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Wang, Baoying, Ruowang Li, and W. Perrizo. Big data analytics in bioinformatics and healthcare. Hershey, PA: Medical Information Science Reference, 2015.

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Natarajan, Prashant, John C. Frenzel, and Detlev H. Smaltz. Demystifying Big Data and Machine Learning for Healthcare. Boca Raton : Taylor & Francis, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315389325.

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Langkafel, Peter, ed. Big Data in Medical Science and Healthcare Management. Berlin, München, Boston: DE GRUYTER, 2015. http://dx.doi.org/10.1515/9783110445381.

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Langkafel, Peter, ed. Big Data in Medical Science and Healthcare Management. Berlin, München, Boston: DE GRUYTER, 2015. http://dx.doi.org/10.1515/9783110445749.

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Saxena, Ankur, Nicolas Brault, and Shazia Rashid. Big Data and Artificial Intelligence for Healthcare Applications. Edited by Ankur Saxena, Nicolas Brault, and Shazia Rashid. First edition. | Boca Raton : CRC Press, 2021.: CRC Press, 2021. http://dx.doi.org/10.1201/9781003093770.

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Book chapters on the topic "Big data in healthcare"

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Karthika, D., and K. Kalaiselvi. "Big Data." In Intelligent Healthcare, 33–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67051-1_3.

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Amirian, Pouria, Francois van Loggerenberg, and Trudie Lang. "Big Data and Big Data Technologies." In Big Data in Healthcare, 39–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62990-2_3.

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Paganoni, Maria Cristina. "Big Data and Healthcare." In Framing Big Data, 59–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16788-2_3.

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Manoj Kumar, M. V., B. S. Prashanth, Aditya Shastry, H. A. Sanjay, and H. R. Sneha. "Healthcare Data Visualization." In Studies in Big Data, 179–211. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0415-7_9.

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Ramírez, Margarita Ramírez, Hilda Beatriz Ramírez Moreno, and Esperanza Manrique Rojas. "Big Data in HealthCare." In Studies in Big Data, 143–59. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8476-8_7.

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Rajbhandari, Sachet, Archana Singh, and Mamta Mittal. "Big Data in Healthcare." In International Conference on Innovative Computing and Communications, 261–69. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2354-6_28.

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Kaul, Deeksha, Harika Raju, and B. K. Tripathy. "Deep Learning in Healthcare." In Studies in Big Data, 97–115. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75855-4_6.

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Subramanian, Kalyanasundaram. "Big Data Analytics and Molecular Medicine." In Healthcare Engineering, 37–42. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3111-3_6.

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Bhardwaj, Priti, and Niyati Baliyan. "Big Data Analytics in Healthcare." In Smart Healthcare Systems, 1–15. Boca Raton : CRC Press, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429020575-1.

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Anand Hareendran, S., and S. S. Vinod Chandra. "Association Rule Mining in Healthcare Analytics." In Data Mining and Big Data, 31–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61845-6_4.

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Conference papers on the topic "Big data in healthcare"

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Kechadi, M.-Tahar. "Healthcare Big Data." In the International Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/3010089.3010143.

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Adenuga, Kayode I., Idris O. Muniru, Fatai I. Sadiq, Rahmat O. Adenuga, and Muhammad J. Solihudeen. "Big Data in Healthcare." In ICSIE '19: 2019 8th International Conference on Software and Information Engineering. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3328833.3328841.

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Rahman, Fuad, Marvin Slepian, and Ari Mitra. "A novel big-data processing framwork for healthcare applications: Big-data-healthcare-in-a-box." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7841018.

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Domova, Veronika, and Shiva Sander-Tavallaey. "Visualization for Quality Healthcare: Patient Flow Exploration." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006351.

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Krause, P. "Big Healthcare Data: Realising the Promise." In Data Analytics 2014: The Rising Role of Big Data. Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/ic.2014.0029.

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Sterling, Mark. "Situated big data and big data analytics for healthcare." In 2017 IEEE Global Humanitarian Technology Conference (GHTC). IEEE, 2017. http://dx.doi.org/10.1109/ghtc.2017.8239322.

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Ambigavathi, M., and D. Sridharan. "Big Data Analytics in Healthcare." In 2018 Tenth International Conference on Advanced Computing (ICoAC). IEEE, 2018. http://dx.doi.org/10.1109/icoac44903.2018.8939061.

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Chen, Guorong, and Mohaiminul Islam. "Big Data Analytics in Healthcare." In 2019 2nd International Conference on Safety Produce Informatization (IICSPI). IEEE, 2019. http://dx.doi.org/10.1109/iicspi48186.2019.9095872.

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Sun, Jimeng, and Chandan K. Reddy. "Big data analytics for healthcare." In KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2487575.2506178.

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Yeng, Prosper Kandabongee, Bian Yang, and Einar Arthur Snekkenes. "Framework for Healthcare Security Practice Analysis, Modeling and Incentivization." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006529.

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Reports on the topic "Big data in healthcare"

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Zwitter, Andrej J., and Amelia Hadfield. Governing Big Data. Librello, January 2014. http://dx.doi.org/10.12924/pag2014.02010001.

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Gildea, Timothy R. Big Data health Physics. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1603973.

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Goldstein, Itay, Chester Spatt, and Mao Ye. Big Data in Finance. Cambridge, MA: National Bureau of Economic Research, March 2021. http://dx.doi.org/10.3386/w28615.

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Big data en salud digital. Chair Alberto Urueña López and José María San Segundo Encinar. ONTSI : Fundación Vodafone España, March 2017. http://dx.doi.org/10.30923/5896-8.

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Alewijn, M. Big data - Banana origin determination. Wageningen: Wageningen Food Safety Research, 2020. http://dx.doi.org/10.18174/516096.

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Farboodi, Maryam, Roxana Mihet, Thomas Philippon, and Laura Veldkamp. Big Data and Firm Dynamics. Cambridge, MA: National Bureau of Economic Research, January 2019. http://dx.doi.org/10.3386/w25515.

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Doucet, Rachel A., Deyan M. Dontchev, Javon S. Burden, and Thomas L. Skoff. Big Data Analytics Test Bed. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada589903.

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Ahrens, James, Jim M. Brase, Bill Hart, Dimitri Kusnezov, and John Shalf. Where Big Data and Prediction Meet. Office of Scientific and Technical Information (OSTI), September 2014. http://dx.doi.org/10.2172/1169890.

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Rathinam, Francis, P. Thissen, and M. Gaarder. Using big data for impact evaluations. Centre of Excellence for Development Impact and Learning (CEDIL), February 2021. http://dx.doi.org/10.51744/cmb2.

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The amount of big data available has exploded with recent innovations in satellites, sensors, mobile devices, call detail records, social media applications, and digital business records. Big data offers great potential for examining whether programmes and policies work, particularly in contexts where traditional methods of data collection are challenging. During pandemics, conflicts, and humanitarian emergency situations, data collection can be challenging or even impossible. This CEDIL Methods Brief takes a step-by-step, practical approach to guide researchers designing impact evaluations based on big data. This brief is based on the CEDIL Methods Working Paper on ‘Using big data for evaluating development outcomes: a systematic map’.
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Francke, Angela, Sven Lißner, and Anke Juliane. Big Data im Radverkehr : Teil II. Technische Universität Dresden, September 2021. http://dx.doi.org/10.26128/2021.241.

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Die Nutzung verfügbarer Radverkehrsdaten auf GPS-Basis stellt eine preisgünstige Möglichkeit für Kommunen dar, einen Überblick über das Nutzungsverhalten ihrer Radfahrenden zu erhalten. Mit den vorliegenden Ergebnissen soll eine Lücke bei der Interpretation von GPS-basierten Daten geschlossen werden. Die Radfahrtypologie auf Basis des geäußerten Verhaltens kann dabei helfen, GPS-Daten auch ohne detaillierte Kenntnisse der zugrundeliegenden Nutzergruppen zielgenauer zu interpretieren. Damit können zukünftig Kommunen die Potenziale entstehender oder bereits vorhandener Angebote an GPS-Radverkehrsdaten zielführender nutzen und ihre Radverkehrsinfrastruktur besser darauf abstimmen. In einem ersten Schritt wurde auf Basis einer Befragung eine empirisch belegte und wissenschaftlich hergeleitete multidimensionale Typologisierung von Radfahrenden erstellt. Anschließend wurde eine umfangreiche heterogene Probandengruppe mit unterschiedlichen soziodemografischen Ausprägungen mit Geräten für die Aufzeichnung ihrer Radrouten ausgestattet. Das auf diesem Weg erhobene Radverkehrsverhalten wurde, gestützt durch kontinuierliche begleitende Befragungen, ausgewertet und anhand unterschiedlicher Indikatoren beschrieben. Damit wurden Präferenzen einzelner Gruppen, z. B. im Hinblick auf Geschwindigkeit, Streckenlänge, Typ der Radverkehrsinfrastruktur, Fahrtzweck oder Routenwahl identifiziert. Auf Basis einer Onlineumfrage konnten vier unterschiedliche Typen von Radfahrenden beschrieben werden, die sich hinsichtlich der Nutzungshäufigkeit, zurückgelegter Entfernungen, Fahrverhalten, Sicherheitsempfinden, Identifikation als Radfahrerende, Wetterabhängigkeit und in motivationalen Aspekten unterscheiden. Anhand der unterschiedlichen Ausprägungen in diesen Merkmalen werden sie als die ambitionierten, die funktionellen, die pragmatischen und die passionierten Radfahrenden bezeichnet. Bezogen auf das Verkehrsverhalten steigt die Nutzungshäufigkeit von ambitionierten über passionierte und pragmatische Radfahrende an. Funktionelle Radfahrende geben die mit Abstand geringste Fahrradnutzung unter allen vier Typen an. Hinsichtlich der angegebenen Distanzen, die zurückgelegt werden, liegen passionierte, pragmatische und funktionelle Radfahrende dicht beieinander. Ambitionierte Radfahrende gaben dagegen an, deutlich größere Distanzen zurückzulegen. Die Ergebnisse aus der Umfrage zeigten sich in einer anschließenden Felduntersuchung in abgeschwächter Form. Insbesondere der ambitionierte Radfahrtyp lässt sich durch höhere Tageskilometerwerte, Geschwindigkeiten und Beschleunigungen von den anderen Typen abgrenzen. Bei den anderen Typen ist eine Unterscheidung weniger ausgeprägt. Hier zeigte sich, dass vor allem die Zugehörigkeit zu einer bestimmten Altersgruppe einen Einfluss auf das Fahrverhalten hat. In Übereinstimmung mit bisherigen Erkenntnissen zeigte sich, dass mit zunehmendem Alter tendenziell etwas langsamer und stetiger gefahren wird. Ebenso radeln auch weibliche Personen etwas langsamer und stetiger als männliche Radfahrer. In der Nutzerbefragung zeigten sich geringe Unterschiede für die Präferenz bei der Infrastrukturnutzung zwischen den Typen, z.B. bei funktionellen Radfahrenden, die eine getrennte Führung im Seitenraum bevorzugen. In der Feldstudie wurde dies ebenfalls untersucht. Auch hier zeigten sich nur geringe Unterschiede. Die Ergebnisse werden auch vor dem Hintergrund eines, eventuell durch die Versuchssituation veränderten Fahrverhaltens der teilnehmenden Radfahrenden, diskutiert. Es konnte vor allem eine hohe Nutzungsfrequenz und Häufigkeit beobachtet werden, die die angegebenen Werte aus der Typenbefragung übertrafen. Für die Nutzung von GPS-Daten für die Radverkehrsplanung wird aus den Ergebnissen abgeleitet, dass eine mögliche Skalierung beziehungsweise Wichtung von Daten entlang soziodemografischer Faktoren die größten Potenziale bietet.
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