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Charalambous A, Dodlek N. Big Data, Machine Learning, and Artificial Intelligence to Advance Cancer Care: Opportunities and Challenges. Semin Oncol Nurs 2023; 39:151429. [PMID: 37085405 DOI: 10.1016/j.soncn.2023.151429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 04/23/2023]
Abstract
OBJECTIVES The rapid advances in artificial intelligence (AI), big data, and machine learning (ML) technologies hold promise for personalized, equitable cancer care and improved health outcomes within the context of cancer and beyond. Furthermore, integrating these technologies into cancer research has been effective in addressing many of the challenges for cancer control and cure. This can be achieved through the insights generated from massive amounts of data, in ways that can help inform decisions, interventions, and precision cancer care. AI, big data, and ML technologies offer, either in isolation or in combination, unconventional pathways that facilitate the better understanding and management of cancer and its impact on the person. The value of AI, big data, and ML technologies has been acknowledged and integrated within the Cancer Moonshot program in the U.S. and the EU Beating Cancer Plan in Europe. DATA SOURCES Relevant studies on the topic have formed the basis for this article. CONCLUSION In a shifting health care environment where cancer care is becoming more complex and demanding, big data and AI technologies can act as a vehicle to facilitating the care continuum. An increasing body of literature demonstrates their impactful contributions in areas such as treatment and diagnosis. These technologies, however, create additional requirements from health care professionals in terms of capacity and preparedness to integrate them effectively and efficiently in clinical practice. Therefore, there is an increasing need for investment and training in oncology to combat and overcome some of the challenges posed by cancer control. IMPLICATIONS FOR NURSING PRACTICE AI, big data, and ML are increasingly integrated in various aspects of health care. As a result, health care professionals, including nurses, will need to adjust in an ever-changing practice environment where these technologies have potential applications in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.
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Affiliation(s)
- Andreas Charalambous
- Associate Professor, Oncology and Palliative Care, Cyprus University of Technology, Osijek, Croatia.
| | - Nikolina Dodlek
- Adjunct Professor, University of Turku, Turku, Finland; Teaching Assistant, Faculty of Dental Medicine and Health, Department of Nursing and Palliative Care, Osijek, Croatia; Unit Manager, Department for Oncology, Clinical Hospital Center, Osijek, Croatia
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Arji G, Ahmadi H, Avazpoor P, Hemmat M. Identifying resilience strategies for disruption management in the healthcare supply chain during COVID-19 by digital innovations: A systematic literature review. INFORMATICS IN MEDICINE UNLOCKED 2023; 38:101199. [PMID: 36873583 PMCID: PMC9957975 DOI: 10.1016/j.imu.2023.101199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 02/12/2023] [Accepted: 02/16/2023] [Indexed: 02/27/2023] Open
Abstract
The worldwide spread of the COVID-19 disease has had a catastrophic effect on healthcare supply chains. The current manuscript systematically analyzes existing studies mitigating strategies for disruption management in the healthcare supply chain during COVID-19. Using a systematic approach, we recognized 35 related papers. Artificial intelligence (AI), block chain, big data analytics, and simulation are the most important technologies employed in supply chain management in healthcare. The findings reveal that the published research has concentrated mainly on generating resilience plans for the management of COVID-19 impacts. Furthermore, the vulnerability of healthcare supply chains and the necessity of establishing better resilience methods are emphasized in most of the research. However, the practical application of these emerging tools for managing disturbance and warranting resilience in the supply chain has been examined only rarely. This article provides directions for additional research, which can guide researchers to develop and conduct impressive studies related to the healthcare supply chain for different disasters.
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Affiliation(s)
- Goli Arji
- Health Information Management, School of Nursing and Midwifery, Saveh University of Medical Sciences, Iran
| | - Hossein Ahmadi
- Centre for Health Technology, Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK
| | - Pejman Avazpoor
- Department of Agriculture Economics, Ferdowsi University of Mashhad, Iran
| | - Morteza Hemmat
- Health Information Management, School of Nursing and Midwifery, Saveh University of Medical Sciences, Iran
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Khan AQ, Nikolov N, Matskin M, Prodan R, Roman D, Sahin B, Bussler C, Soylu A. Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines. SENSORS (BASEL, SWITZERLAND) 2023; 23:564. [PMID: 36679360 PMCID: PMC9863399 DOI: 10.3390/s23020564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/15/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Big data pipelines are developed to process data characterized by one or more of the three big data features, commonly known as the three Vs (volume, velocity, and variety), through a series of steps (e.g., extract, transform, and move), making the ground work for the use of advanced analytics and ML/AI techniques. Computing continuum (i.e., cloud/fog/edge) allows access to virtually infinite amount of resources, where data pipelines could be executed at scale; however, the implementation of data pipelines on the continuum is a complex task that needs to take computing resources, data transmission channels, triggers, data transfer methods, integration of message queues, etc., into account. The task becomes even more challenging when data storage is considered as part of the data pipelines. Local storage is expensive, hard to maintain, and comes with several challenges (e.g., data availability, data security, and backup). The use of cloud storage, i.e., storage-as-a-service (StaaS), instead of local storage has the potential of providing more flexibility in terms of scalability, fault tolerance, and availability. In this article, we propose a generic approach to integrate StaaS with data pipelines, i.e., computation on an on-premise server or on a specific cloud, but integration with StaaS, and develop a ranking method for available storage options based on five key parameters: cost, proximity, network performance, server-side encryption, and user weights/preferences. The evaluation carried out demonstrates the effectiveness of the proposed approach in terms of data transfer performance, utility of the individual parameters, and feasibility of dynamic selection of a storage option based on four primary user scenarios.
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Affiliation(s)
- Akif Quddus Khan
- Department of Computer Science, Norwegian University of Science and Technology—NTNU, 2815 Gjøvik, Norway
| | | | - Mihhail Matskin
- Department of Computer Science, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden
| | - Radu Prodan
- Department of Information Technology, University of Klagenfurt, 9020 Klagenfurt, Austria
| | | | - Bekir Sahin
- Logistics Management, National University of Science and Technology, 111 Sohar, Oman
| | | | - Ahmet Soylu
- Department of Computer Science, OsloMet—Oslo Metropolitan University, 0167 Oslo, Norway
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Hotspot Mining in the Field of Library and Information Science under the Environment of Big Data. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2022; 2022:2802835. [PMID: 35958385 PMCID: PMC9357669 DOI: 10.1155/2022/2802835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 06/30/2022] [Accepted: 07/06/2022] [Indexed: 11/17/2022]
Abstract
Currently, with the implementation of big data strategies in countries all over the world, big data has achieved vigorous development in various fields. Big data research and application practices have also rapidly attracted the attention of the library and information field. Objective. The study explored the current state of research and research hotspots of big data in the library and information field and further discussed the future research trends. Methods. In the CNKI database, 16 CSSCI source journals in the discipline of library information and digital library were selected as data sources, and the relevant literature was retrieved with the theme of “big data.” The collected literature was excluded and expanded according to the citation relationship. Then, with the help of Bicomb and SPSS, co-word analysis and cluster analysis would be carried out on these literature results. Results. According to the findings of the data analysis, the research hotspots on the topic mainly focus on five major research themes, namely, big data and smart library, big data and intelligence research, data mining and cloud computing, big data and information analysis, and library innovation and services. Limitations. At present, the research scope and coverage on this topic are wide, which leads to the research still staying at the macro level. Conclusions. Big data research will remain one of the hotspots in the future. However, the most study is still limited to the perspective of library and information and has not yet analyzed the research status, research hotspots, and development trends in this field from the perspective of big data knowledge structure. Moreover, machine learning, artificial intelligence, knowledge services, AR, and VR may be new directions for future attention and development.
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Cozzoli N, Salvatore FP, Faccilongo N, Milone M. How can big data analytics be used for healthcare organization management? Literary framework and future research from a systematic review. BMC Health Serv Res 2022; 22:809. [PMID: 35733192 PMCID: PMC9213639 DOI: 10.1186/s12913-022-08167-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 06/06/2022] [Indexed: 12/11/2022] Open
Abstract
Background Multiple attempts aimed at highlighting the relationship between big data analytics and benefits for healthcare organizations have been raised in the literature. The big data impact on health organization management is still not clear due to the relationship’s multi-disciplinary nature. This study aims to answer three research questions: a) What is the state of art of big data analytics adopted by healthcare organizations? b) What about the benefits for both health managers and healthcare organizations? c) What about future directions on big data analytics research in healthcare? Methods Through a systematic literature review the impact of big data analytics on healthcare management has been examined. The study aims to map extant literature and present a framework for future scholars to further build on, and executives to be guided by. Results The positive relationship between big data analytics and healthcare organization management has emerged. To find out common elements in the studies reviewed, 16 studies have been selected and clustered into 4 research areas: 1) Potentialities of big data analytics. 2) Resource management. 3) Big data analytics and management of health surveillance systems. 4) Big data analytics and technology for healthcare organization. Conclusions In conclusion is identified how the big data analytics solutions are considered a milestone for managerial studies applied to healthcare organizations, although scientific research needs to investigate standardization and integration of the devices as well as the protocol in data analysis to improve the performance of the healthcare organization. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-022-08167-z.
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Affiliation(s)
- Nicola Cozzoli
- Department of Economics, University of Foggia, Via Caggese n.1, Foggia, Italy
| | | | - Nicola Faccilongo
- Department of Economics, University of Foggia, Via Caggese n.1, Foggia, Italy
| | - Michele Milone
- Department of Economics, University of Foggia, Via Caggese n.1, Foggia, Italy
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Green Transition: The Frontier of the Digicircular Economy Evidenced from a Systematic Literature Review. SUSTAINABILITY 2021. [DOI: 10.3390/su131911068] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Today, the issue of economic circularity is certainly not a new concept. It represents an essential issue in any production system since it is an alternative to the current production and consumption model. The importance of the topic is confirmed worldwide. However, there is still a “circularity gap” that can be bridged in the short and medium term, probably with the use of innovative and digital technologies. In fact, many researchers agree that the sustainable future can be achieved in the long term thanks to digital technologies (i.e., IoT, artificial intelligence, quantum computing etc.) which, thanks to their speed of calculation, are able to identify the right solutions at the right time. The challenge, therefore, will be to develop innovative technologies and tools for the efficient use of resources in industries for sustainable production. Thus, the aim of this study is to define the current state of the art and future research developments in this very promising field. To achieve this goal, the integration of a “set” of tools, based on the AHP method and the PRISMA protocol, is proposed. The results aim to be a guideline for decision makers and researchers interested in this topic.
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