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Article
Publication date: 1 March 1997

Ian M. Hughes, John D. Holden and Andrea M. Tree

Background: Many audits in primary care can be criticized because of the absence of verifiable data to measure outcomes, and the lack of a non‐participating group against…

Abstract

Background: Many audits in primary care can be criticized because of the absence of verifiable data to measure outcomes, and the lack of a non‐participating group against which to compare results. Objective: Using Prescribing Analyses and Cost (PACT) data to quantify the effect of an audit in 15 practices. We sought to quantify the effect of the audit of benzodiazepine prescribing in a district by measuring the detailed changes in prescribing in participating practices before, during and after audit, and by comparing the volume of prescribing of these drugs in participating and neighbouring non‐participating practices. Methods: At the start of the audit, 291 993 patients in the Sefton district of North West England were registered with 55 general practices. Fifteen practices, caring for 87 902 patients, took part in an audit of benzodiazepine prescribing. We analysed routinely‐collected prescribing data to assess trends in benzodiazepine prescribing for those practices which took part in the audit and the remaining (non‐participatory) practices in the district. Main measures: The number of defined daily doses of benzodiazepine prescribed by those general practitioners auditing their prescribing of these drugs during the audit. The volume of benzodiazepines prescribed by all general practitioners in Sefton during the quarter immediately before and the quarter immediately after the audit. Results: There was a significant reduction in the number of defined daily doses dispensed for temazepam, nitrazepam, and lorazepam during the audit. There was a significantly greater reduction in the number of items prescribed by those doctors who took part in the audit than their colleagues who did not. Conclusions: An audit of benzodiazepine prescribing achieved a significant reduction in the volume of these drugs dispensed. An analysis of routinely‐collected data can usefully measure the result of an audit of prescribing.

Details

Journal of Clinical Effectiveness, vol. 2 no. 3
Type: Research Article
ISSN: 1361-5874

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Article
Publication date: 1 June 2013

Jaime E. Gómez M.

Vernacular transformations of underused places give shape to Ephemeral Urban Dwellings (EUD). By reading the spatial patterns of use of three of these buildings, this…

Abstract

Vernacular transformations of underused places give shape to Ephemeral Urban Dwellings (EUD). By reading the spatial patterns of use of three of these buildings, this paper demonstrates that EUD replicate the way activities and ideas of privacy are related to space in the previous and permanent homes left behind by its inhabitants. The case studies are located in central areas of Bogotá and, although ephemeral, they have stayed for years. Personal interviews and mental maps drawn by the interviewees as well as on site drawings and photography by the author are the main sources of this study.

The paper recalls the processes of cultural appropriation that take place when people adjust to new cultural contexts. In the case of the dwellings studied, these processes give clues on how the ideas that shape the way people use space are translated into new places. The paper's conclusion calls for further research on EUD as an object of academic interest.

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Open House International, vol. 38 no. 2
Type: Research Article
ISSN: 0168-2601

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Article
Publication date: 20 November 2017

Andreas Behr and Jurij Weinblat

The purpose of this paper is to do a performance comparison of three different data mining techniques.

Abstract

Purpose

The purpose of this paper is to do a performance comparison of three different data mining techniques.

Design/methodology/approach

Logit model, decision tree and random forest are applied in this study on British, French, German, Italian, Portuguese and Spanish balance sheet data from 2006 to 2012, which covers 446,464 firms. Because of the strong imbalance with regard to the solvency status, classification trees and random forests are modified to adapt to this imbalance. All three model specifications are optimized extensively using resampling techniques, relying on the training sample only. Model performance is assessed, strictly, based on out-of-sample predictions.

Findings

Random forest is found to strongly outperform the classification tree and the logit model in almost all considered years and countries, according to the quality measure in this study.

Originality/value

Obtaining reliable estimates of default propensity scores is of immense importance for potential credit grantors, portfolio managers and regulatory authorities. As the overwhelming majority of firms are not listed on stock exchanges, annual balance sheets still provide the most important source of information. The obtained ranking of the three models according to their predictive performance is relatively robust, due to the consideration of several countries and a relatively long time period.

Details

The Journal of Risk Finance, vol. 18 no. 5
Type: Research Article
ISSN: 1526-5943

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Article
Publication date: 23 July 2020

Angelica Lo Duca and Andrea Marchetti

Ship route prediction (SRP) is a quite complicated task, which enables the determination of the next position of a ship after a given period of time, given its current…

Abstract

Purpose

Ship route prediction (SRP) is a quite complicated task, which enables the determination of the next position of a ship after a given period of time, given its current position. This paper aims to describe a study, which compares five families of multiclass classification algorithms to perform SRP.

Design/methodology/approach

Tested algorithm families include: Naive Bayes (NB), nearest neighbors, decision trees, linear algorithms and extension from binary. A common structure for all the algorithm families was implemented and adapted to the specific case, according to the test to be done. The tests were done on one month of real data extracted from automatic identification system messages, collected around the island of Malta.

Findings

Experiments show that K-nearest neighbors and decision trees algorithms outperform all the other algorithms. Experiments also demonstrate that linear algorithms and NB have a very poor performance.

Research limitations/implications

This study is limited to the area surrounding Malta. Thus, findings cannot be generalized to every context. However, the methodology presented is general and can help other researchers in this area to choose appropriate methods for their problems.

Practical implications

The results of this study can be exploited by applications for maritime surveillance to build decision support systems to monitor and predict ship routes in a given area. For example, to protect the marine environment, the use of SRP techniques could be used to protect areas at risk such as marine protected areas, from illegal fishing.

Originality/value

The paper proposes a solid methodology to perform tests on SRP, based on a series of important machine learning algorithms for the prediction.

Details

Journal of Systems and Information Technology, vol. 12 no. 3
Type: Research Article
ISSN: 1328-7265

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Book part
Publication date: 15 March 2021

Jochen Hartmann

Across disciplines, researchers and practitioners employ decision tree ensembles such as random forests and XGBoost with great success. What explains their popularity…

Abstract

Across disciplines, researchers and practitioners employ decision tree ensembles such as random forests and XGBoost with great success. What explains their popularity? This chapter showcases how marketing scholars and decision-makers can harness the power of decision tree ensembles for academic and practical applications. The author discusses the origin of decision tree ensembles, explains their theoretical underpinnings, and illustrates them empirically using a real-world telemarketing case, with the objective of predicting customer conversions. Readers unfamiliar with decision tree ensembles will learn to appreciate them for their versatility, competitive accuracy, ease of application, and computational efficiency and will gain a comprehensive understanding why decision tree ensembles contribute to every data scientist's methodological toolbox.

Details

The Machine Age of Customer Insight
Type: Book
ISBN: 978-1-83909-697-6

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Article
Publication date: 19 November 2018

M. Simona Andreano, Roberto Benedetti, Andrea Mazzitelli, Federica Piersimoni and Davide Di Fatta

This paper aims to introduce a new framework that helps to get an overview of contextual factors that influence the ability of small- and medium-sized enterprises (SMEs…

Abstract

Purpose

This paper aims to introduce a new framework that helps to get an overview of contextual factors that influence the ability of small- and medium-sized enterprises (SMEs) to survive the economic crisis in a business cluster, as parts of a system.

Design/methodology/approach

The spatial autologistic model and the logit regression tree (RT) were applied to SME manufacturing companies localized in the business clusters of the Italian Marche region to explain interconnection among the actors of the network and their heterogeneous behavior with the environment.

Findings

The main findings of the application confirm that contextual influences are decisive in the definition of firm’s survival, explained through the presence of spatial dependence in bankruptcy analysis, validating the transmission effects of corporate bankruptcy within the business clusters in the Marche region.

Originality/value

The estimation of the logistic RT allowed to identify sub-systems, homogeneous with respect to crucial context variables, with different firms’ behaviors in terms of probability to survive in the system and relation to their environment. Therefore, a systemic approach is required to provide a better understanding of such kind of phenomena.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

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Book part
Publication date: 9 November 2020

Gracie Irvine, Natasha Pauli, Renata Varea and Bryan Boruff

The Ba River catchment and delta on the island of Viti Levu, Fiji, supports a wealth of livelihoods and is populated by diverse communities who are living with an…

Abstract

The Ba River catchment and delta on the island of Viti Levu, Fiji, supports a wealth of livelihoods and is populated by diverse communities who are living with an increased frequency and intensity of hydro-meteorological hazards (floods, cyclones and droughts). Participatory mapping as part of focus group discussions is a tool that can be used to elucidate communities’ understanding of the differing impacts of multiple hazards, as well as the strategies used to prepare and respond to different hazards. In this chapter, the authors present the results of qualitative research undertaken with members of three communities along the Ba River, from the Nausori highlands to the coastal mangroves, with a particular focus on recent floods (2009, 2012) and Tropical Cyclone Winston (2016). The communities draw on a wide range of livelihood strategies from fishing and agriculture to tourism and outside work. Natural hazard events vary in their impact on these livelihood strategies across the landscape and seascape, so that community members can adjust their activities accordingly. The temporal ‘signatures’ of ongoing impacts are also variable across communities and resources. The results suggest that taking a broad, landscape (and seascape) approach to understanding how communities draw livelihoods is valuable in informing effective and inclusive adaptation strategies for environmental change. Furthermore, documenting how the landscape is used in a mapped output may be a valuable tool for future social impact assessment for resource extraction activities.

Details

Climate-Induced Disasters in the Asia-Pacific Region: Response, Recovery, Adaptation
Type: Book
ISBN: 978-1-83909-987-8

Keywords

Content available
Article
Publication date: 8 July 2019

Daniel Abreu Vasconcellos de Paula, Rinaldo Artes, Fabio Ayres and Andrea Maria Accioly Fonseca Minardi

Although credit unions are nonprofit organizations, their objectives depend on the efficient management of their resources and credit risk aligned with the principles of…

Abstract

Purpose

Although credit unions are nonprofit organizations, their objectives depend on the efficient management of their resources and credit risk aligned with the principles of the cooperative doctrine. This paper aims to propose the combined use of credit scoring and profit scoring to increase the effectiveness of the loan-granting process in credit unions.

Design/methodology/approach

This sample is composed by the data of personal loans transactions of a Brazilian credit union.

Findings

The analysis reveals that the use of statistical methods improves significantly the predictability of default when compared to the use of subjective techniques and the superiority of the random forests model in estimating credit scoring and profit scoring when compared to logit and ordinary least squares method (OLS) regression. The study also illustrates how both analyses can be used jointly for more effective decision-making.

Originality/value

Replacing subjective analysis with objective credit analysis using deterministic models will benefit Brazilian credit unions. The credit decision will be based on the input variables and on clear criteria, turning the decision-making process impartial. The joint use of credit scoring and profit scoring allows granting credit for the clients with the highest potential to pay debt obligation and, at the same time, to certify that the transaction profitability meets the goals of the organization: to be sustainable and to provide loans and investment opportunities at attractive rates to members.

Details

RAUSP Management Journal, vol. 54 no. 3
Type: Research Article
ISSN: 2531-0488

Keywords

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Article
Publication date: 1 April 2004

Georgios I. Zekos

Investigates the differences in protocols between arbitral tribunals and courts, with particular emphasis on US, Greek and English law. Gives examples of each country and…

Abstract

Investigates the differences in protocols between arbitral tribunals and courts, with particular emphasis on US, Greek and English law. Gives examples of each country and its way of using the law in specific circumstances, and shows the variations therein. Sums up that arbitration is much the better way to gok as it avoids delays and expenses, plus the vexation/frustration of normal litigation. Concludes that the US and Greek constitutions and common law tradition in England appear to allow involved parties to choose their own judge, who can thus be an arbitrator. Discusses e‐commerce and speculates on this for the future.

Details

Managerial Law, vol. 46 no. 2/3
Type: Research Article
ISSN: 0309-0558

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Article
Publication date: 2 January 2020

Thomas Kundinger, Phani Krishna Yalavarthi, Andreas Riener, Philipp Wintersberger and Clemens Schartmüller

Drowsiness is a common cause of severe road accidents. Therefore, numerous drowsiness detection methods were developed and explored in recent years, especially concepts…

Abstract

Purpose

Drowsiness is a common cause of severe road accidents. Therefore, numerous drowsiness detection methods were developed and explored in recent years, especially concepts using physiological measurements achieved promising results. Nevertheless, existing systems have some limitations that hinder their use in vehicles. To overcome these limitations, this paper aims to investigate the development of a low-cost, non-invasive drowsiness detection system, using physiological signals obtained from conventional wearable devices.

Design/methodology/approach

Two simulator studies, the first study in a low-level driving simulator (N = 10) to check feasibility and efficiency, and the second study in a high-fidelity driving simulator (N = 30) including two age groups, were conducted. An algorithm was developed to extract features from the heart rate signals and a data set was created by labelling these features according to the identified driver state in the simulator study. Using this data set, binary classifiers were trained and tested using various machine learning algorithms.

Findings

The trained classifiers reached a classification accuracy of 99.9%, which is similar to the results obtained by the studies which used intrusive electrodes to detect ECG. The results revealed that heart rate patterns are sensitive to the drivers’ age, i.e. models trained with data from one age group are not efficient in detecting drowsiness for another age group, suggesting to develop universal driver models with data from different age groups combined with individual driver models.

Originality/value

This work investigated the feasibility of driver drowsiness detection by solely using physiological data from wrist-worn wearable devices, such as smartwatches or fitness trackers that are readily available in the consumer market. It was found that such devices are reliable in drowsiness detection.

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