Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: Implementing supply chain quality management
Abstract: This paper describes a strategic framework for the development of supply chain quality management (SCQM). The framework integrates both vision- and gap-driven change approaches to evaluate not only the implementation gaps but also their potential countermeasures. Based on literature review, drivers of supply chain quality are identified. They are: supply chain competence, critical success factors (CSF), strategic components, and SCQ practices/activities/programmes. Based on SCQM literature, five survey items are also presented in this study for each drive. The Analytic Hierarchy Process (AHP) is used to develop priority indices for these survey items. Knowledge of these critical dimensions and possible implementation discrepancies could help multinational enterprises and their supply chain partners lay out effective and efficient SCQM plans.
Extracted statement — SCM field: Supply chain quality management
Research field: Science
Title: Identifying critical enablers and pathways to high performance supply chain quality management
Abstract: Purpose – The aim of this paper is threefold: first, to examine the content of supply chain quality management (SCQM); second, to identify the structure of SCQM; and third, to show ways for finding improvement opportunities and organizing individual institution's resources/actions into collective performance outcomes.Design/methodology/approach – To meet the goals of this work, the paper uses abductive reasoning and two qualitative methods: content analysis and formal concept analysis (FCA). Primary data were collected from both original design manufacturers (ODMs) and original equipment manufacturers (OEMs) in Taiwan.Findings – According to the qualitative empirical study, modern enterprises need to pay immediate attention to the following two pathways: a compliance approach and a voluntary approach. For the former, three strategic content variables are identified: training programs, ISO, and supplier quality audit programs. As for initiating a voluntary effort, modern lead firms need to instill “motivat...
Extracted statement — SCM field: Supply chain quality management
Research field: Science
Title: Automated Detection of Postoperative Surgical Site Infections Using Supervised Methods with Electronic Health Record Data
Abstract: The National Surgical Quality Improvement Project (NSQIP) is widely recognized as “the best in the nation” surgical quality improvement resource in the United States. In particular, it rigorously defines postoperative morbidity outcomes, including surgical adverse events occurring within 30 days of surgery. Due to its manual yet expensive construction process, the NSQIP registry is of exceptionally high quality, but its high cost remains a significant bottleneck to NSQIP’s wider dissemination. In this work, we propose an automated surgical adverse events detection tool, aimed at accelerating the process of extracting postoperative outcomes from medical charts. As a prototype system, we combined local EHR data with the NSQIP gold standard outcomes and developed machine learned models to retrospectively detect Surgical Site Infections (SSI), a particular family of adverse events that NSQIP extracts. The built models have high specificity (from 0.788 to 0.988) as well as very high negative predictive values (>0.98), reliably eliminating the vast majority of patients without SSI, thereby significantly reducing the NSQIP extractors’ burden.
Extracted statement — Infection: Surgical Site Infection
Research field: Science
Title: Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data
Abstract: This study describes a novel approach to solve the surgical site infection (SSI) classification problem. Feature engineering has traditionally been one of the most important steps in solving complex classification problems, especially in cases with temporal data. The described novel approach is based on abstraction of temporal data recorded in three temporal windows. Maximum likelihood L1-norm (lasso) regularization was used in penalized logistic regression to predict the onset of surgical site infection occurrence based on available patient blood testing results up to the day of surgery. Prior knowledge of predictors (blood tests) was integrated in the modelling by introduction of penalty factors depending on blood test prices and an early stopping parameter limiting the maximum number of selected features used in predictive modelling. Finally, solutions resulting in higher interpretability and cost-effectiveness were demonstrated. Using repeated holdout cross-validation, the baseline C-reactive protein (CRP) classifier achieved a mean AUC of 0.801, whereas our best full lasso model achieved a mean AUC of 0.956. Best model testing results were achieved for full lasso model with maximum number of features limited at 20 features with an AUC of 0.967. Presented models showed the potential to not only support domain experts in their decision making but could also prove invaluable for improvement in prediction of SSI occurrence, which may even help setting new guidelines in the field of preoperative SSI prevention and surveillance.
Extracted statement — Infection: Surgical Site Infection
Research field: Science
Title: An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples
Abstract: A large fraction of the electronic health records consists of clinical measurements collected over time, such as blood tests, which provide important information about the health status of a patient. These sequences of clinical measurements are naturally represented as time series, characterized by multiple variables and the presence of missing data, which complicate analysis. In this work, we propose a surgical site infection detection framework for patients undergoing colorectal cancer surgery that is completely unsupervised, hence alleviating the problem of getting access to labelled training data. The framework is based on powerful kernels for multivariate time series that account for missing data when computing similarities. Our approach show superior performance compared to baselines that have to resort to imputation techniques and performs comparable to a supervised classification baseline.
Extracted statement — Infection: Surgical Site Infection
Research field: Science
Title: A diagnostic algorithm for the surveillance of deep surgical site infections after colorectal surgery
Abstract: Abstract Objective: Surveillance of surgical site infections (SSIs) is important for infection control and is usually performed through retrospective manual chart review. The aim of this study was to develop an algorithm for the surveillance of deep SSIs based on clinical variables to enhance efficiency of surveillance. Design: Retrospective cohort study (2012–2015). Setting: A Dutch teaching hospital. Participants: We included all consecutive patients who underwent colorectal surgery excluding those with contaminated wounds at the time of surgery. All patients were evaluated for deep SSIs through manual chart review, using the Centers for Disease Control and Prevention (CDC) criteria as the reference standard. Analysis: We used logistic regression modeling to identify predictors that contributed to the estimation of diagnostic probability. Bootstrapping was applied to increase generalizability, followed by assessment of statistical performance and clinical implications. Results: In total, 1,606 patients were included, of whom 129 (8.0%) acquired a deep SSI. The final model included postoperative length of stay, wound class, readmission, reoperation, and 30-day mortality. The model achieved 68.7% specificity and 98.5% sensitivity and an area under the receiver operator characteristic (ROC) curve (AUC) of 0.950 (95% CI, 0.932–0.969). Positive and negative predictive values were 21.5% and 99.8%, respectively. Applying the algorithm resulted in a 63.4% reduction in the number of records requiring full manual review (from 1,606 to 590). Conclusions: This 5-parameter model identified 98.5% of patients with a deep SSI. The model can be used to develop semiautomatic surveillance of deep SSIs after colorectal surgery, which may further improve efficiency and quality of SSI surveillance.
Extracted statement — Infection: Surgical Site Infection
Research field: Science
Title: Improving Prediction of Surgical Site Infection Risk with Multilevel Modeling
Abstract: Background Surgical site infection (SSI) surveillance is a key factor in the elaboration of strategies to reduce SSI occurrence and in providing surgeons with appropriate data feedback (risk indicators, clinical prediction rule). Aim To improve the predictive performance of an individual-based SSI risk model by considering a multilevel hierarchical structure. Patients and Methods Data were collected anonymously by the French SSI active surveillance system in 2011. An SSI diagnosis was made by the surgical teams and infection control practitioners following standardized criteria. A random 20% sample comprising 151 hospitals, 502 wards and 62280 patients was used. Three-level (patient, ward, hospital) hierarchical logistic regression models were initially performed. Parameters were estimated using the simulation-based Markov Chain Monte Carlo procedure. Results A total of 623 SSI were diagnosed (1%). The hospital level was discarded from the analysis as it did not contribute to variability of SSI occurrence (p = 0.32). Established individual risk factors (patient history, surgical procedure and hospitalization characteristics) were identified. A significant heterogeneity in SSI occurrence between wards was found (median odds ratio [MOR] 3.59, 95% credibility interval [CI] 3.03 to 4.33) after adjusting for patient-level variables. The effects of the follow-up duration varied between wards (p<10−9), with an increased heterogeneity when follow-up was <15 days (MOR 6.92, 95% CI 5.31 to 9.07]). The final two-level model significantly improved the discriminative accuracy compared to the single level reference model (p<10−9), with an area under the ROC curve of 0.84. Conclusion This study sheds new light on the respective contribution of patient-, ward- and hospital-levels to SSI occurrence and demonstrates the significant impact of the ward level over and above risk factors present at patient level (i.e., independently from patient case-mix).
Extracted statement — Infection: Surgical Site Infection
Research field: Science
Title: Predictive Modeling of Surgical Site Infections Using Sparse Laboratory Data
Abstract: <jats:p>As part of a data mining competition, a training and test set of laboratory test data about patients with and without surgical site infection (SSI) were provided. The task was to develop predictive models with training set and identify patients with SSI in the no label test set. Lab test results are vital resources that guide healthcare providers make decisions about all aspects of surgical patient management. Many machine learning models were developed after pre-processing and imputing the lab tests data and only the top performing methods are discussed. Overall, RANDOM FOREST algorithms performed better than Support Vector Machine and Logistic Regression. Using a set of 74 lab tests, with RF, there were only 4 false positives in the training set and predicted 35 out of 50 SSI patients in the test set (Accuracy 0.86, Sensitivity 0.68, and Specificity 0.91). Optimal ways to address healthcare data quality concerns and imputation methods as well as newer generalizable algorithms need to be explored further to decipher new associations and knowledge among laboratory biomarkers and SSI.</jats:p>
Extracted statement — Infection: Surgical Site Infection
Research field: Science
Title: Data-driven Temporal Prediction of Surgical Site Infection
Abstract: Analysis of data from Electronic Health Records (EHR) presents unique challenges, in particular regarding nonuniform temporal resolution of longitudinal variables. A considerable amount of patient information is available in the EHR - including blood tests that are performed routinely during inpatient follow-up. These data are useful for the design of advanced machine learning-based methods and prediction models. Using a matched cohort of patients undergoing gastrointestinal surgery (101 cases and 904 controls), we built a prediction model for post-operative surgical site infections (SSIs) using Gaussian process (GP) regression, time warping and imputation methods to manage the sparsity of the data source, and support vector machines for classification. For most blood tests, wider confidence intervals after imputation were obtained in patients with SSI. Predictive performance with individual blood tests was maintained or improved by joint model prediction, and non-linear classifiers performed consistently better than linear models.
Extracted statement — Infection: Surgical Site Infection
Research field: Science
Title: Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks
Abstract: Clinical measurements that can be represented as time series constitute an important fraction of the electronic health records and are often both uncertain and incomplete. Recurrent neural networks are a special class of neural networks that are particularly suitable to process time series data but, in their original formulation, cannot explicitly deal with missing data. In this paper, we explore imputation strategies for handling missing values in classifiers based on recurrent neural network (RNN) and apply a recently proposed recurrent architecture, the Gated Recurrent Unit with Decay, specifically designed to handle missing data. We focus on the problem of detecting surgical site infection in patients by analyzing time series of their blood sample measurements and we compare the results obtained with different RNN-based classifiers.
Extracted statement — Infection: Surgical Site Infection
Research field: Science
Title: Understanding the Success Factors of Sustainable Supply Chain Management: Empirical Evidence from the Electrics and Electronics Industry
Abstract: Recent studies have reported that organizations are often unable to identify the key success factors of Sustainable Supply Chain Management (SSCM) and to understand their implications for management practice. For this reason, the implementation of SSCM often does not result in noticeable benefits. So far, research has failed to offer any explanations for this discrepancy. In view of this fact, our study aims at identifying and analyzing the factors that underlie successful SSCM. Success factors are identified by means of a systematic literature review and are then integrated into an explanatory model. Consequently, the proposed success factor model is tested on the basis of an empirical study focusing on recycling networks of the electrics and electronics industry. We found that signaling, information provision and the adoption of standards are crucial preconditions for strategy commitment, mutual learning, the establishment of ecological cycles and hence for the overall success of SSCM. Copyright © 2011 John Wiley & Sons, Ltd and ERP Environment.
Extracted statement — SCM field: Sustainable supply chain
Research field: Science
Title: An Exploratory Study of the Success Factors for Extranet Adoption in E-Supply Chain
Abstract: Extranet is an enabler/system that enriches the information service quality in e-supply chain. This paper uses factor analysis to determine four extranet success factors: system quality, information quality, service quality, and work performance quality. A critical analysis of areas that require improvement is also conducted.
Extracted statement — Critical success factors: System quality
Research field: Science
Title: Critical success factors for B2B e‐commerce use within the UK NHS pharmaceutical supply chain
Abstract: Purpose – The purpose of this paper is to determine those factors perceived by users to influence the successful on‐going use of e‐commerce systems in business‐to‐business (B2B) buying and selling transactions through examination of the views of individuals acting in both purchasing and selling roles within the UK National Health Service (NHS) pharmaceutical supply chain.Design/methodology/approach – Literature from the fields of operations and supply chain management (SCM) and information systems (IS) is used to determine candidate factors that might influence the success of the use of e‐commerce. A questionnaire based on these is used for primary data collection in the UK NHS pharmaceutical supply chain. Factor analysis is used to analyse the data.Findings – The paper yields five composite factors that are perceived by users to influence successful e‐commerce use. “System quality,” “information quality,” “management and use,” “world wide web – assurance and empathy,” and “trust” are proposed as potentia...
Extracted statement — Critical success factors: System quality
Research field: Science
Title: A study of supplier selection factors for high-tech industries in the supply chain
Abstract: Amid the intensive competition among global industries, the relationship between manufacturers and suppliers has turned from antagonist to cooperative. Through partnerships, both parties can be mutually benefited, and the key factor that maintains such relationship lies in how manufacturers select proper suppliers. The purpose of this study is to explore the key factors considered by manufacturers in supplier selection and the relationships between these factors. Through a literature review, eight supplier selection factors, comprising price response capability, quality management capability, technological capability, delivery capability, flexible capability, management capability, commercial image, and financial capability are derived. Based on the theoretic foundation proposed by previous researchers, a causal model of supplier selection factors is further constructed. The results of a survey on high-tech industries are used to verify the relationships between the eight factors using structural equation modelling (SEM). Based on the empirical results, conclusions and suggestions are finally proposed as a reference for manufacturers and suppliers.
Extracted statement — Critical success factors: technological capability
Research field: Science
Title: Automatic Test Data Generation Based on Ant Colony Optimization
Abstract: Software testing is a crucial measure used to assure the quality of software. Path testing can detect bugs earlier because of it performs higher error coverage. This paper presents a model of generating test data based on an improved ant colony optimization and path coverage criteria. Experiments show that the algorithm has a better performance than other two algorithms and improve the efficiency of test data generation notably.
Extracted statement — Fields: Test Data Generation
Research field: Science
Title: An Econometric Analysis for CO<sub>2</sub> Emissions, Energy Consumption, Economic Growth, Foreign Trade and Urbanization of Japan
Abstract: This paper examines the dynamic causal relationship between carbon dioxide emissions, energy consumption, economic growth, foreign trade and urbanization using time series data for the period of 1960-2009. Short-run unidirectional causalities are found from energy consumption and trade openness to carbon dioxide emissions, from trade openness to energy consumption, from carbon dioxide emissions to economic growth, and from economic growth to trade openness. The test results also support the evidence of existence of long-run relationship among the variables in the form of Equation (1) which also conform the results of bounds and Johansen conintegration tests. It is found that over time higher energy consumption in Japan gives rise to more carbon dioxide emissions as a result the environment will be polluted more. But in respect of economic growth, trade openness and urbanization the environmental quality is found to be normal good in the long-run.
Extracted statement — Type of data: Time series
Research field: Science
Title: An econometric study of carbon dioxide (CO2) emissions, energy consumption, and economic growth of Pakistan
Abstract: Purpose – The purpose of this paper is to examine the relationship among environmental pollution, economic growth and energy consumption per capita in the case of Pakistan. The per capital carbon dioxide (CO2) emission is used as the environmental indicator, the commercial energy use per capita as the energy consumption indicator, and the per capita gross domestic product (GDP) as the economic indicator.Design/methodology/approach – The investigation is made on the basis of the environmental Kuznets curve (EKC), using time series data from 1971 to 2006, by applying different econometric tools like ADF Unit Root Johansen Co‐integration VECM and Granger causality tests.Findings – The Granger causality test shows that there is a long term relationship between these three indicators, with bidirectional causality between per capita CO2 emission and per capita energy consumption. A monotonically increasing curve between GDP and CO2 emission has been found for the sample period, rejecting the EKC relationship, i...
Extracted statement — Type of data: Time series
Research field: Science
Title: Environmental Kuznets curve in an open economy: a bounds testing and causality analysis for Tunisia
Abstract: The aim of this paper is to investigate the existence of environmental Kuznets curve (EKC) in an open economy like Tunisia using annual time series data for the period of 1971-2010. The ARDL bounds testing approach to cointegration is applied to test long run relationship in the presence of structural breaks and vector error correction model (VECM) to detect the causality among the variables. The robustness of causality analysis has been tested by applying the innovative accounting approach (IAA). The findings of this paper confirmed the long run relationship between economic growth, energy consumption, trade openness and CO2 emissions in Tunisian Economy. The results also indicated the existence of EKC confirmed by the VECM and IAA approaches. The study has significant contribution for policy implications to curtail energy pollutants by implementing environment friendly regulations to sustain the economic development in Tunisia.
Extracted statement — Type of data: Time series
Research field: Science
Title: Critical success factors of web-based supply-chain management systems: an exploratory study
Abstract: This paper reports the results of a survey on the critical success factors (CSFs) of web-based supply-chain management systems (WSCMS). An empirical study was conducted and an exploratory factor analysis of the survey data revealed five major dimensions of the CSFs for WSCMS implementation, namely (1) communication, (2) top management commitment, (3) data security, (4) training and education, and (5) hardware and software reliability. The findings of the results provide insights for companies using or planning to use WSCMS.
Extracted statement — Critical success factors: Top management commitment