Exploring the Potential of Predictive Analytics and Big Data in Emergency Care

Janke, Alexander T.; Overbeek, Daniel L.; Kocher, Keith E.; Levy, Phillip D.

doi:10.1016/j.annemergmed.2015.06.024

Cited by 92 publications

(70 citation statements)

References 77 publications

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“…Data collection only occurred during a single episode in each centre and does not account for seasonal variation in presentations or admission rates. The simplicity of GAPS when compared with machine learning or Artificial Intelligence solutions may limit its accuracy but does aid its implementation 22. While accuracy could be increased by focusing of specific disease groups, this would limit ease of use and integration with current systems 23 24…”

Section: Discussionmentioning

confidence: 99%

Comparison of Glasgow Admission Prediction Score and Amb Score in predicting need for inpatient care

et al. 2018

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Section: Discussionmentioning

confidence: 99%

Comparison of Glasgow Admission Prediction Score and Amb Score in predicting need for inpatient care

et al. 2018

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“…Janke et al [20] found some surprising benefits of predictive analytics and big data in emergency care. They note that many large emergency departments have more than 100,000 patient visits per year that through electronic medical records of all procedures carried out in modern emergency care will create (very) large amounts of real-world observational data.…”

Section: Predictive Analyticsmentioning

confidence: 98%

Fuzzy Entropy Used for Predictive Analytics

Carlsson

Heikkilä

Mezei

2016

Fuzzy Logic in Its 50th Year

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Process interruptions in (very) large production systems are difficult to deal with. Modern processes are highly automated; data is collected with sensor technology that forms a big data context and offers challenges to identify coming failures from the very large sets of data. The sensors collect huge amounts of data but the failure events are few and infrequent and hard to find (and even harder to predict). In this article, our goal is to develop models for predictive maintenance in a big data environment. The purpose of feature selection in the context of predictive maintenance is to identify a small set of process diagnostics that are sufficient to predict future failures. We apply interval-valued fuzzy sets and various entropy measures defined on them to perform feature selection on process diagnostics. We show how these models can be utilized as the basis of decision support systems in process industries to aid predictive maintenance.

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“…Besides the well-documented risks arising from the collection, and subsequent processing, of personally identifying information, the main risks of organizing data from the health provider's perspective relate to the opportunities of organizing big data. In other words, the loss of opportunity and risks to value of unified data-driven health services not being developed due to the quality, variability, and veracity of the data collected (Janke, Daniel, Overbeek, Kocher, & Levy, 2016;Kambatla, Kollias, Kumar, & Grama, 2014). In addition, there will be resistance to the development of such unified data-driven services (Costa, 2014) from patients and others (Janke et al, 2016), citing privacy and other concerns.…”

Section: Organizing Processes: Value and Risksmentioning

confidence: 99%

Data work in context: Value, risks, and governance

Foster

McLeod

Nolin

et al. 2018

Asso for Info Science & Tech

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While always integral to scientific activity, data work has recently emerged as a key set of processes within societal activities of all kinds. While data work presents new opportunities for discovery, value creation, and decision making, its emergence also raises significant ethical issues, including those of ownership, privacy, and trust. This article presents a review of data work, and how negotiating a trade‐off between its value and risks requires locating its processes within the contexts of its conditions and consequences. These include international, national, and sectoral conditions of law, policy, and regulation at a macro level; organizational conditions of information and data governance that aim to address the value and risks of data work at a meso level; along with attention to the everyday contexts of data and information handling by data information and other professionals at a micro level. In conclusion, a conceptual framework is presented that locates the processes of data work within the matrix of its macro meso and micro conditions, its consequences for individuals, organizations, and society, and the relations between them. Suggestions are given for how research into the study of data work—its value risks and governance— can be advanced by using this framework.

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Exploring the Potential of Predictive Analytics and Big Data in Emergency Care

Cited by 92 publications

References 77 publications

Comparison of Glasgow Admission Prediction Score and Amb Score in predicting need for inpatient care

Comparison of Glasgow Admission Prediction Score and Amb Score in predicting need for inpatient care

Fuzzy Entropy Used for Predictive Analytics

Data work in context: Value, risks, and governance

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