Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities
Abstract Clinical decisions are more promising and evidence-based, hence, big data analytics to assist clinical decision-making has been expressed for a variety of clinical fields. Due to the sheer size and availability of healthcare data, big data analytics has revolutionized this industry and prom...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rehman, Arshia [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia systems - Springer Berlin Heidelberg, 1993, 28(2021), 4 vom: 21. Jan., Seite 1339-1371 |
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| Übergeordnetes Werk: |
volume:28 ; year:2021 ; number:4 ; day:21 ; month:01 ; pages:1339-1371 |
| Links: |
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| DOI / URN: |
10.1007/s00530-020-00736-8 |
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OLC2079245244 |
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| 520 | |a Abstract Clinical decisions are more promising and evidence-based, hence, big data analytics to assist clinical decision-making has been expressed for a variety of clinical fields. Due to the sheer size and availability of healthcare data, big data analytics has revolutionized this industry and promises us a world of opportunities. It promises us the power of early detection, prediction, prevention, and helps us to improve the quality of life. Researchers and clinicians are working to inhibit big data from having a positive impact on health in the future. Different tools and techniques are being used to analyze, process, accumulate, assimilate, and manage large amount of healthcare data either in structured or unstructured form. In this review, we address the need of big data analytics in healthcare: why and how can it help to improve life?. We present the emerging landscape of big data and analytical techniques in the five sub-disciplines of healthcare, i.e., medical image analysis and imaging informatics, bioinformatics, clinical informatics, public health informatics and medical signal analytics. We present different architectures, advantages and repositories of each discipline that draws an integrated depiction of how distinct healthcare activities are accomplished in the pipeline to facilitate individual patients from multiple perspectives. Finally, the paper ends with the notable applications and challenges in adoption of big data analytics in healthcare. | ||
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10.1007/s00530-020-00736-8 doi (DE-627)OLC2079245244 (DE-He213)s00530-020-00736-8-p DE-627 ger DE-627 rakwb eng 004 VZ 24,1 ssgn 54.00 bkl Rehman, Arshia verfasserin aut Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract Clinical decisions are more promising and evidence-based, hence, big data analytics to assist clinical decision-making has been expressed for a variety of clinical fields. Due to the sheer size and availability of healthcare data, big data analytics has revolutionized this industry and promises us a world of opportunities. It promises us the power of early detection, prediction, prevention, and helps us to improve the quality of life. Researchers and clinicians are working to inhibit big data from having a positive impact on health in the future. Different tools and techniques are being used to analyze, process, accumulate, assimilate, and manage large amount of healthcare data either in structured or unstructured form. In this review, we address the need of big data analytics in healthcare: why and how can it help to improve life?. We present the emerging landscape of big data and analytical techniques in the five sub-disciplines of healthcare, i.e., medical image analysis and imaging informatics, bioinformatics, clinical informatics, public health informatics and medical signal analytics. We present different architectures, advantages and repositories of each discipline that draws an integrated depiction of how distinct healthcare activities are accomplished in the pipeline to facilitate individual patients from multiple perspectives. Finally, the paper ends with the notable applications and challenges in adoption of big data analytics in healthcare. Big data analytics Medical image processing and Imaging informatics Bioinformatics and Genomics Clinical informatics Public health informatics Medical signal analytics Naz, Saeeda aut Razzak, Imran aut Enthalten in Multimedia systems Springer Berlin Heidelberg, 1993 28(2021), 4 vom: 21. Jan., Seite 1339-1371 (DE-627)171214684 (DE-600)1166499-X (DE-576)03871857X 0942-4962 nnns volume:28 year:2021 number:4 day:21 month:01 pages:1339-1371 https://doi.org/10.1007/s00530-020-00736-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-MKW SSG-OLC-BUB SSG-OPC-BBI GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 54.00 VZ AR 28 2021 4 21 01 1339-1371 |
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Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities |
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leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities |
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Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities |
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Abstract Clinical decisions are more promising and evidence-based, hence, big data analytics to assist clinical decision-making has been expressed for a variety of clinical fields. Due to the sheer size and availability of healthcare data, big data analytics has revolutionized this industry and promises us a world of opportunities. It promises us the power of early detection, prediction, prevention, and helps us to improve the quality of life. Researchers and clinicians are working to inhibit big data from having a positive impact on health in the future. Different tools and techniques are being used to analyze, process, accumulate, assimilate, and manage large amount of healthcare data either in structured or unstructured form. In this review, we address the need of big data analytics in healthcare: why and how can it help to improve life?. We present the emerging landscape of big data and analytical techniques in the five sub-disciplines of healthcare, i.e., medical image analysis and imaging informatics, bioinformatics, clinical informatics, public health informatics and medical signal analytics. We present different architectures, advantages and repositories of each discipline that draws an integrated depiction of how distinct healthcare activities are accomplished in the pipeline to facilitate individual patients from multiple perspectives. Finally, the paper ends with the notable applications and challenges in adoption of big data analytics in healthcare. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
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Abstract Clinical decisions are more promising and evidence-based, hence, big data analytics to assist clinical decision-making has been expressed for a variety of clinical fields. Due to the sheer size and availability of healthcare data, big data analytics has revolutionized this industry and promises us a world of opportunities. It promises us the power of early detection, prediction, prevention, and helps us to improve the quality of life. Researchers and clinicians are working to inhibit big data from having a positive impact on health in the future. Different tools and techniques are being used to analyze, process, accumulate, assimilate, and manage large amount of healthcare data either in structured or unstructured form. In this review, we address the need of big data analytics in healthcare: why and how can it help to improve life?. We present the emerging landscape of big data and analytical techniques in the five sub-disciplines of healthcare, i.e., medical image analysis and imaging informatics, bioinformatics, clinical informatics, public health informatics and medical signal analytics. We present different architectures, advantages and repositories of each discipline that draws an integrated depiction of how distinct healthcare activities are accomplished in the pipeline to facilitate individual patients from multiple perspectives. Finally, the paper ends with the notable applications and challenges in adoption of big data analytics in healthcare. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
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Abstract Clinical decisions are more promising and evidence-based, hence, big data analytics to assist clinical decision-making has been expressed for a variety of clinical fields. Due to the sheer size and availability of healthcare data, big data analytics has revolutionized this industry and promises us a world of opportunities. It promises us the power of early detection, prediction, prevention, and helps us to improve the quality of life. Researchers and clinicians are working to inhibit big data from having a positive impact on health in the future. Different tools and techniques are being used to analyze, process, accumulate, assimilate, and manage large amount of healthcare data either in structured or unstructured form. In this review, we address the need of big data analytics in healthcare: why and how can it help to improve life?. We present the emerging landscape of big data and analytical techniques in the five sub-disciplines of healthcare, i.e., medical image analysis and imaging informatics, bioinformatics, clinical informatics, public health informatics and medical signal analytics. We present different architectures, advantages and repositories of each discipline that draws an integrated depiction of how distinct healthcare activities are accomplished in the pipeline to facilitate individual patients from multiple perspectives. Finally, the paper ends with the notable applications and challenges in adoption of big data analytics in healthcare. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
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