Environmental effects on acute exacerbations of respiratory diseases: A real-world big data study

Purpose The purpose of this paper is to develop a novel network and dynamic data envelopment analysis (DEA) model for evaluating sustainability of supply chains. In the proposed model, all links can be considered in calculation of efficiency score. Design/methodology/approach A dynamic DEA model to evaluate sustainable supply chains in which networks have series structure is proposed. Nature of free links is defined and subsequently applied in calculating relative efficiency of supply chains. An additive network DEA model is developed to evaluate sustainability of supply chains in several periods. A case study demonstrates applicability of proposed approach. Findings This paper assists managers to identify inefficient supply chains and take proper remedial actions for performance optimization. Besides, overall efficiency scores of supply chains have less fluctuation. By utilizing the proposed model and determining dual-role factors, managers can plan their supply chains properly and more accurately. Research limitations/implications In real world, managers face with big data. Therefore, we need to develop an approach to deal with big data. Practical implications The proposed model offers useful managerial implications along with means for managers to monitor and measure efficiency of their production processes. The proposed model can be applied in real world problems in which decision makers are faced with multi-stage processes such as supply chains, production systems, etc. Originality/value For the first time, the authors present additive model of network-dynamic DEA. For the first time, the authors outline the links in a way that carry-overs of networks are connected in different periods and not in different stages.

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Big Data Processing and Mining for Next Generation Intelligent Transportation Systems

Jurnal Teknologi ◽

10.11113/jt.v63.1949 ◽

2013 ◽

Vol 63 (3) ◽

Cited By ~ 13

Author(s):

Jelena Fiosina ◽

Maxims Fiosins, Jörg P. Müller

Keyword(s):

Big Data ◽

Data Processing ◽

Real World ◽

Intelligent Transportation Systems ◽

Social Impact ◽

Intelligent Transportation ◽

Transportation Systems ◽

Optimization Techniques ◽

Next Generation ◽

Big Data Processing

The deployment of future Internet and communication technologies (ICT) provide intelligent transportation systems (ITS) with huge volumes of real-time data (Big Data) that need to be managed, communicated, interpreted, aggregated and analysed. These technologies considerably enhance the effectiveness and user friendliness of ITS, providing considerable economic and social impact. Real-world application scenarios are needed to derive requirements for software architecture and novel features of ITS in the context of the Internet of Things (IoT) and cloud technologies. In this study, we contend that future service- and cloud-based ITS can largely benefit from sophisticated data processing capabilities. Therefore, new Big Data processing and mining (BDPM) as well as optimization techniques need to be developed and applied to support decision-making capabilities. This study presents real-world scenarios of ITS applications, and demonstrates the need for next-generation Big Data analysis and optimization strategies. Decentralised cooperative BDPM methods are reviewed and their effectiveness is evaluated using real-world data models of the city of Hannover, Germany. We point out and discuss future work directions and opportunities in the area of the development of BDPM methods in ITS.

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The ABCs of Rhinoviruses, Wheezing, and Asthma

Journal of Virology ◽

10.1128/jvi.02290-09 ◽

2010 ◽

Vol 84 (15) ◽

pp. 7418-7426 ◽

Cited By ~ 168

Author(s):

James E. Gern

Keyword(s):

Respiratory Diseases ◽

Chronic Obstructive Lung Disease ◽

Allergic Diseases ◽

Chronic Respiratory Disease ◽

Chronic Obstructive ◽

Respiratory Illnesses ◽

Chronic Respiratory Diseases ◽

Acute Exacerbations ◽

Close Relationship ◽

Viral Diagnostics

ABSTRACT Human rhinoviruses (HRVs) were discovered as common cold pathogens over 50 years ago. Recent advances in molecular viral diagnostics have led to an appreciation of their role in more-significant respiratory illnesses, including bronchiolitis in infancy, childhood pneumonia, and acute exacerbations of chronic respiratory diseases such as asthma, chronic obstructive lung disease, and cystic fibrosis. Until a few years ago, only two groups of HRVs (A and B) had been recognized. However, full and partial sequencing of HRVs led to the discovery of a third species of HRV (HRV-C) that has distinct structural and biologic features. Risk factors and pathogenic mechanisms for more-severe HRV infections are being defined, and yet fundamental questions persist about mechanisms relating this common pathogen to allergic diseases and asthma. The close relationship between HRV infections and asthma suggests that antiviral treatments could have a major impact on the morbidity associated with this chronic respiratory disease.

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Ethics of identity in the time of big data

First Monday ◽

10.5210/fm.v24i5.9624 ◽

2019 ◽

Author(s):

James Brusseau

Keyword(s):

Big Data ◽

Personal Identity ◽

Real World ◽

Natural Law ◽

The Other ◽

Ethical Question ◽

Human Relationships ◽

The Real ◽

Ethical Frameworks ◽

Big Data Technology

Compartmentalizing our distinct personal identities is increasingly difficult in big data reality. Pictures of the person we were on past vacations resurface in employers’ Google searches; LinkedIn which exhibits our income level is increasingly used as a dating web site. Whether on vacation, at work, or seeking romance, our digital selves stream together. One result is that a perennial ethical question about personal identity has spilled out of philosophy departments and into the real world. Ought we possess one, unified identity that coherently integrates the various aspects of our lives, or, incarnate deeply distinct selves suited to different occasions and contexts? At bottom, are we one, or many? The question is not only palpable today, but also urgent because if a decision is not made by us, the forces of big data and surveillance capitalism will make it for us by compelling unity. Speaking in favor of the big data tendency, Facebook’s Mark Zuckerberg promotes the ethics of an integrated identity, a single version of selfhood maintained across diverse contexts and human relationships. This essay goes in the other direction by sketching two ethical frameworks arranged to defend our compartmentalized identities, which amounts to promoting the dis-integration of our selves. One framework connects with natural law, the other with language, and both aim to create a sense of selfhood that breaks away from its own past, and from the unifying powers of big data technology.

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Real-world antibiotic use in treating acute exacerbations of chronic obstructive pulmonary disease (AECOPD) in China: Evidence from the ACURE study

Frontiers in Pharmacology ◽

10.3389/fphar.2021.649884 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yiming Ma ◽

Ke Huang ◽

Chen Liang ◽

Xihua Mao ◽

Yaowen Zhang ◽

...

Keyword(s):

Chronic Obstructive Pulmonary Disease ◽

Pulmonary Disease ◽

Real World ◽

Antibiotic Use ◽

Tertiary Hospital ◽

Chronic Obstructive ◽

Oral Antibiotics ◽

Obstructive Pulmonary Disease ◽

Acute Exacerbations ◽

Hospital Group

Background: The evidence for real-world antibiotic use in treating acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is insufficient. This study aimed to investigate real-world antibiotic use in the management of AECOPD in China.Methods: All hospitalized AECOPD patients from the acute exacerbation of chronic obstructive pulmonary disease inpatient registry (ACURE) study conducted at 163 sites between January 2018 and December 2019 were screened according to the eligible criteria. The eligible study population was divided into secondary and tertiary hospital groups. Patients’ baseline characteristics, antibiotic use, and bacterial pathogen characteristics were retrieved and analyzed using SPSS 23.0.Results: A total of 1663 patients were included in the study, including 194 patients from secondary hospitals and 1469 patients from tertiary hospitals. Among the 1663 AECOPD patients enrolled, 1434 (86.2%) received antibiotic treatment, comprising approximately 85.6% and 86.3% of patients in the secondary and tertiary hospital groups, respectively. The median antibiotic therapy duration was 9.0 (interquartile range [IQR]: 7.0 - 11.0)°days. Regarding the routes of antibiotic use, 1400 (97.6%) patients received intravenous antibiotics, 18 (1.3%) patients received oral antibiotics, 15 (1.0%) patients received both intravenous and oral antibiotics, and one (0.1%) patient received both oral and nebulized antibiotic treatment. In addition, cephalosporin, penicillin, and quinolone were the most commonly prescribed antibiotics (43.6%, 37.0%, and 34.2%, respectively). In total, 990 (56.5%) patients underwent pathogen examinations; the proportion of patients receiving pathogen examinations in the second hospital group was significantly lower than that in the tertiary hospital group (46.4% vs 61.3%, p < 0.001).Conclusion: This study demonstrates that an antibiotic overuse may exist in the treatment of AECOPD in China. Measures should be taken to prevent the overuse of antibiotics and potential antimicrobial resistance (AMR) in Chinese AECOPD patients.

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Power of big data to improve patient care in gastroenterology

Frontline Gastroenterology ◽

10.1136/flgastro-2019-101239 ◽

2021 ◽

pp. flgastro-2019-101239

Author(s):

Jamie Catlow ◽

Benjamin Bray ◽

Eva Morris ◽

Matt Rutter

Keyword(s):

Machine Learning ◽

Big Data ◽

Real World ◽

Common Good ◽

Treatment Effectiveness ◽

Healthcare Delivery ◽

Improve Patient Care ◽

Patient Registries ◽

General Data Protection Regulation ◽

Improve Patient

Big data is defined as being large, varied or frequently updated, and usually generated from real-world interaction. With the unprecedented availability of big data, comes an obligation to maximise its potential for healthcare improvements in treatment effectiveness, disease prevention and healthcare delivery. We review the opportunities and challenges that big data brings to gastroenterology. We review its sources for healthcare improvement in gastroenterology, including electronic medical records, patient registries and patient-generated data. Big data can complement traditional research methods in hypothesis generation, supporting studies and disseminating findings; and in some cases holds distinct advantages where traditional trials are unfeasible. There is great potential power in patient-level linkage of datasets to help quantify inequalities, identify best practice and improve patient outcomes. We exemplify this with the UK colorectal cancer repository and the potential of linkage using the National Endoscopy Database, the inflammatory bowel disease registry and the National Health Service bowel cancer screening programme. Artificial intelligence and machine learning are increasingly being used to improve diagnostics in gastroenterology, with image analysis entering clinical practice, and the potential of machine learning to improve outcome prediction and diagnostics in other clinical areas. Big data brings issues with large sample sizes, real-world biases, data curation, keeping clinical context at analysis and General Data Protection Regulation compliance. There is a tension between our obligation to use data for the common good and protecting individual patient’s data. We emphasise the importance of engaging with our patients to enable them to understand their data usage as fully as they wish.

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Need for Dynamicity in Social Networking Sites

Digital Arts and Entertainment ◽

10.4018/978-1-4666-6114-1.ch003 ◽

2014 ◽

pp. 64-87

Author(s):

Gurdeep S Hura

Keyword(s):

Data Mining ◽

Social Media ◽

Big Data ◽

Social Networking ◽

Real World ◽

Social Networking Sites ◽

Data Science ◽

Emerging Technology ◽

New Applications ◽

Future Status

This chapter presents this new emerging technology of social media and networking with a detailed discussion on: basic definitions and applications, how this technology evolved in the last few years, the need for dynamicity under data mining environment. It also provides a comprehensive design and analysis of popular social networking media and sites available for the users. A brief discussion on the data mining methodologies for implementing the variety of new applications dealing with huge/big data in data science is presented. Further, an attempt is being made in this chapter to present a new emerging perspective of data mining methodologies with its dynamicity for social networking media and sites as a new trend and needed framework for dealing with huge amount of data for its collection, analysis and interpretation for a number of real world applications. A discussion will also be provided for the current and future status of data mining of social media and networking applications.

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