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18 - Big Data, Cyber Security and Liberty
- Edited by Bruce Arrigo, University of North Carolina, Charlotte, Brian Sellers, Eastern Michigan University
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- Book:
- The Pre-Crime Society
- Published by:
- Bristol University Press
- Published online:
- 14 April 2023
- Print publication:
- 30 July 2021, pp 409-432
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- Chapter
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Summary
Introduction
The growth of digital technology has created opportunities for individuals to interact and share information in real-time using any combination of text, image and video (Wall, 2001; Newman & Clarke, 2003; Holt & Bossler, 2016; Lee & Holt, 2020). There are now vast quantities of data generated from humans’ use of various technologies, providing both industry personnel and academic researchers with an alternative way to examine social phenomena (Silverman, 2013; Holt, 2017). In fact, there is now distinct terminology used to reference such data, commonly called ‘big data’, which is generally defined as voluminous datasets that cannot be ‘perceived, acquired, managed, and processed by traditional information technology and software/hardware tools within a tolerable time’ (Chen et al, 2014, p. 173). Big data is increasingly used by interested stakeholders to illustrate trends and patterns that confirm and/or challenge wider assumptions (Laney, 2001; Khoury & Ioannidis, 2014; Wu, Zhu, Wu & Ding, 2014; Liu, Li, Li & Wu, 2016; Williams et al, 2017; Song, Song & Lee, 2018; Lee & Holt, 2020).
Researchers across numerous disciplines examine big data sets to address various topics (see Sensmeier, 2015; Lee & Holt, 2020). Nursing scholars, for example, use big data to improve patientclinician communications, as well as improve their awareness of emerging health issues within the wider population (Sensmeier, 2015). Real-time access to patient information provides nurses with the ability to make optimal clinical decisions regardless of care setting, satisfying an integral part of the profession (Sensmeier, 2015). Similarly, criminologists and other social scientists use big data to assess offenders’ motivations, methods and target preferences on various computer-mediated communications (CMC) platforms (D’Ovidio et al, 2009; Maimon et al, 2014; Song et al, 2018). Exploring these sources provide deeper insights at the individual and aggregate level to understand both offender perspectives and potential targets for victimization (Lee & Holt, 2020). In essence, big data allows interested stakeholders to make data-driven predictions and better-informed decisions that can result in more favorable outcomes and effective strategies (McAfee & Brynjolfsson, 2012).
While analyses of big data have the ability to reveal various trends and patterns that can be explored to support and/or challenge wider assumptions, the data on its own does not enable researchers to make any inferences (Smith, Bennett Moses & Chan, 2017; Song, Song & Lee, 2018; Lee & Holt, 2020).
Changes in the palliative performance scale may be as important as the initial palliative performance scale for predicting survival in terminal cancer patients
- Guk Jin Lee, Ji Hyun Gwak, Myoung Sim Kim, Mi Yeong Lee, Seo Ree Kim, Sang Hoon Chun, Jong Youl Jin
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- Journal:
- Palliative & Supportive Care / Volume 19 / Issue 5 / October 2021
- Published online by Cambridge University Press:
- 07 May 2021, pp. 547-551
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- Article
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Objective
The accurate estimation of expected survival in terminal cancer patients is important. The palliative performance scale (PPS) is an important factor in predicting survival of hospice patients. The purpose of this study was to examine how initial status of PPS and changes in PPS affect the survival of hospice patients in Korea.
MethodWe retrospectively examined 315 patients who were admitted to our hospice unit between January 2017 and December 2018. The patients were divided based on the PPS of ≥50% (group A) and ≤40% (group B). We performed survival analysis for factors associated with the length of survival (LOS) in group A. Based on the hospice team's weekly evaluation of PPS, we examined the effect of initial levels and changes in group A on the prognosis of patients who survived for 2 weeks or more.
ResultsAt the time of admission to hospice, 265 (84.1%) patients were PPS ≥50%, and 50 (15.9%) were PPS ≤40%. The median LOS of PPS ≥50% and PPS ≤40% were 15 (2–158 days) and 9 (2–43 days), respectively. Male, gastrointestinal cancer, and lower initial PPS all predicted poor prognosis in group A. Male, gastrointestinal cancer, and a PPS change of 10% or greater, compared with initial status 1 week and 2 weeks of hospitalization, were all predictors of poor prognosis in group A patients who survived for 2 weeks or longer.
Significance of resultsOur research demonstrates the significance of PPS change at 1 week and 2 weeks, suggesting the importance of evaluating not only initial PPS but also change in PPS.