Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: A qualitative study of the determinants of self-managing team effectiveness in a scrum team
Abstract: There are many evidences in the literature that the use self-managing teams has positive impacts on several dimensions of team effectiveness. Agile methods, supported by the Agile Manifesto, defend the use of self-managing teams in software development in substitution of hierarchically managed, traditional teams. The goal of this research was to study how a self-managing software team works in practice and how the behaviors of the software organization support or hinder the effectiveness of such teams. We performed a single case holistic case study, looking in depth into the actual behavior of a mature Scrum team in industry. Using interviews and participant observation, we collected qualitative data from five team members in several interactions. We extract the behavior of the team and of the software company in terms of the determinants of self-managing team effectiveness defined in a theoretical model from the literature. We found evidence that 17 out of 24 determinants of this model exist in the studied context. We concluded that certain determinants can support or facilitate the adoption of methodologies like Scrum, while the use of Scrum may affect other determinants.
Extracted statement — Agile
Method: Scrum
Research field: Science
Title: Investigating the Long-Term Acceptance of Agile Methodologies: An Empirical Study of Developer Perceptions in Scrum Projects
Abstract: Agile development methodologies have gained great interest in research and practice. As their introduction considerably changes traditional working habits of developers, the long-term acceptance of agile methodologies becomes a critical success factor. Yet, current studies primarily examine the early adoption stage of agile methodologies. To investigate the long-term acceptance, we conducted a study at a leading insurance company that introduced Scrum in 2007. Using a qualitative research design and the Diffusion of Innovations Theory as a lens for analysis, we gained in-depth insights into factors influencing the acceptance of Scrum. Particularly, developers felt Scrum to be more compatible to their actual working practices. Moreover, they perceived the use of Scrum to deliver numerous relative advantages. However, we also identified possible barriers to acceptance since developers felt both the complexity of Scrum and the required discipline to be higher in comparison with traditional development methodologies.
Extracted statement — Agile
Method: Scrum
Research field: Science
Title: High-speed segmentation-driven high-resolution matching
Abstract: This paper proposes a segmentation-based approach for matching of high-resolution stereo images in real time. The approach employs direct region matching in a raster scan fashion influenced by scanline approaches, but with pixel decoupling. To enable real-time performance it is implemented as a heterogeneous system of an FPGA and a sequential processor. Additionally, the approach is designed for low resource usage in order to qualify as part of unified image processing in an embedded system.
Extracted statement — Taxonomy stage: Step: Segmentation
Research field: Science
Title: Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction
Abstract: <jats:p><jats:italic>Objective</jats:italic>. Achieving accurate prediction of sepsis detection moment based on bedside monitor data in the intensive care unit (ICU). A good clinical outcome is more probable when onset is suspected and treated on time, thus early insight of sepsis onset may save lives and reduce costs. <jats:italic>Methodology</jats:italic>. We present a novel approach for feature extraction, which focuses on the hypothesis that unstable patients are more prone to develop sepsis during ICU stay. These features are used in machine learning algorithms to provide a prediction of a patient’s likelihood to develop sepsis during ICU stay, hours before it is diagnosed. <jats:italic>Results</jats:italic>. Five machine learning algorithms were implemented using R software packages. The algorithms were trained and tested with a set of 4 features which represent the variability in vital signs. These algorithms aimed to calculate a patient’s probability to become septic within the next 4 hours, based on recordings from the last 8 hours. The best area under the curve (AUC) was achieved with Support Vector Machine (SVM) with radial basis function, which was 88.38%. <jats:italic>Conclusions</jats:italic>. The high level of predictive accuracy along with the simplicity and availability of input variables present great potential if applied in ICUs. Variability of a patient’s vital signs proves to be a good indicator of one’s chance to become septic during ICU stay.</jats:p>
Extracted statement — Infection: Sepsis
Research field: Science
Title: Machine-Learning-Based Laboratory Developed Test for the Diagnosis of Sepsis in High-Risk Patients
Abstract: Sepsis, a dysregulated host response to infection, is a major health burden in terms of both mortality and cost. The difficulties clinicians face in diagnosing sepsis, alongside the insufficiencies of diagnostic biomarkers, motivate the present study. This work develops a machine-learning-based sepsis diagnostic for a high-risk patient group, using a geographically and institutionally diverse collection of nearly 500,000 patient health records. Using only a minimal set of clinical variables, our diagnostics outperform common severity scoring systems and sepsis biomarkers and benefit from being available immediately upon ordering.
Extracted statement — Infection: Sepsis
Research field: Science
Title: Physiological monitoring for critically ill patients: testing a predictive model for the early detection of sepsis
Abstract: OBJECTIVE To assess the predictive value for the early detection of sepsis of the physiological monitoring parameters currently recommended by the Surviving Sepsis Campaign. METHODS The Project IMPACT data set was used to assess whether the physiological parameters of heart rate, mean arterial pressure, body temperature, and respiratory rate can be used to distinguish between critically ill adult patients with and without sepsis in the first 24 hours of admission to an intensive care unit. RESULTS All predictor variables used in the analyses differed significantly between patients with sepsis and patients without sepsis. However, only 2 of the predictor variables, mean arterial pressure and high temperature, were independently associated with sepsis. In addition, the temperature mean for hypothermia was significantly lower in patients without sepsis. The odds ratio for having sepsis was 2.126 for patients with a temperature of 38 degrees C or higher, 3.874 for patients with a mean arterial blood pressure of less than 70 mm Hg, and 4.63 times greater for patients who had both of these conditions. CONCLUSIONS The results support the use of some of the guidelines of the Surviving Sepsis Campaign. However, the lowest mean temperature was significantly less for patients without sepsis than for patients with sepsis, a finding that calls into question the clinical usefulness of using hypothermia as an early predictor of sepsis. Alone the group of variables used is not sufficient for discriminating between critically ill patients with and without sepsis.
Extracted statement — Infection: Sepsis
Research field: Science
Title: Predictive models for severe sepsis in adult ICU patients
Abstract: Intensive Care Unit (ICU) patients have significant morbidity and mortality, often from complications that arise during the hospital stay. Severe sepsis is one of the leading causes of death among these patients. Predictive models have the potential to allow for earlier detection of severe sepsis and ultimately earlier intervention. However, current methods for identifying and predicting severe sepsis are biased and inadequate. The goal of this work is to identify a new framework for the prediction of severe sepsis and identify early predictors utilizing clinical laboratory values and vital signs collected in adult ICU patients. We explore models with logistic regression (LR), support vector machines (SVM), and logistic model trees (LMT) utilizing vital signs, laboratory values, or a combination of vital and laboratory values. When applied to a retrospective cohort of ICU patients, the SVM model using laboratory and vital signs as predictors identified 339 (65%) of the 3,446 patients as developing severe sepsis correctly. Based on this new framework and developed models, we provide a recommendation for the use in clinical decision support in ICU and non-ICU environments.
Extracted statement — Infection: Sepsis
Research field: Science
Title: A Bayesian network for early diagnosis of sepsis patients: a basis for a clinical decision support system
Abstract: Sepsis is a severe medical condition caused by an inordinate immune response to an infection. Early detection of sepsis symptoms is important to prevent the progression into the more severe stages of the disease, which kills one in four it effects. Electronic medical records of 1492 patients containing 233 cases of sepsis were used in a clustering analysis to identify features that are indicative of sepsis and can be further used for training a Bayesian inference network. The Bayesian network was constructed using the systemic inflammatory response syndrome criteria, mean arterial pressure, and lactate levels for sepsis patients. The resulting network reveals a clear correlation between lactate levels and sepsis. Furthermore, it was shown that lactate levels may be predicative of the SIRS criteria. In this light, Bayesian networks of sepsis patients hold the promise of providing a clinical decision support system in the future.
Extracted statement — Infection: Sepsis
Research field: Science
Title: From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system
Abstract: OBJECTIVE To develop a decision support system to identify patients at high risk for hyperlactatemia based upon routinely measured vital signs and laboratory studies. MATERIALS AND METHODS Electronic health records of 741 adult patients at the University of California Davis Health System who met at least two systemic inflammatory response syndrome criteria were used to associate patients' vital signs, white blood cell count (WBC), with sepsis occurrence and mortality. Generative and discriminative classification (naïve Bayes, support vector machines, Gaussian mixture models, hidden Markov models) were used to integrate heterogeneous patient data and form a predictive tool for the inference of lactate level and mortality risk. RESULTS An accuracy of 0.99 and discriminability of 1.00 area under the receiver operating characteristic curve (AUC) for lactate level prediction was obtained when the vital signs and WBC measurements were analysed in a 24 h time bin. An accuracy of 0.73 and discriminability of 0.73 AUC for mortality prediction in patients with sepsis was achieved with only three features: median of lactate levels, mean arterial pressure, and median absolute deviation of the respiratory rate. DISCUSSION This study introduces a new scheme for the prediction of lactate levels and mortality risk from patient vital signs and WBC. Accurate prediction of both these variables can drive the appropriate response by clinical staff and thus may have important implications for patient health and treatment outcome. CONCLUSIONS Effective predictions of lactate levels and mortality risk can be provided with a few clinical variables when the temporal aspect and variability of patient data are considered.
Extracted statement — Infection: Sepsis
Research field: Science
Title: Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction
Abstract: <jats:p><jats:italic>Objective</jats:italic>. Achieving accurate prediction of sepsis detection moment based on bedside monitor data in the intensive care unit (ICU). A good clinical outcome is more probable when onset is suspected and treated on time, thus early insight of sepsis onset may save lives and reduce costs. <jats:italic>Methodology</jats:italic>. We present a novel approach for feature extraction, which focuses on the hypothesis that unstable patients are more prone to develop sepsis during ICU stay. These features are used in machine learning algorithms to provide a prediction of a patient’s likelihood to develop sepsis during ICU stay, hours before it is diagnosed. <jats:italic>Results</jats:italic>. Five machine learning algorithms were implemented using R software packages. The algorithms were trained and tested with a set of 4 features which represent the variability in vital signs. These algorithms aimed to calculate a patient’s probability to become septic within the next 4 hours, based on recordings from the last 8 hours. The best area under the curve (AUC) was achieved with Support Vector Machine (SVM) with radial basis function, which was 88.38%. <jats:italic>Conclusions</jats:italic>. The high level of predictive accuracy along with the simplicity and availability of input variables present great potential if applied in ICUs. Variability of a patient’s vital signs proves to be a good indicator of one’s chance to become septic during ICU stay.</jats:p>
Extracted statement — Objective: Sepsis
Research field: Science
Title: Machine-Learning-Based Laboratory Developed Test for the Diagnosis of Sepsis in High-Risk Patients
Abstract: Sepsis, a dysregulated host response to infection, is a major health burden in terms of both mortality and cost. The difficulties clinicians face in diagnosing sepsis, alongside the insufficiencies of diagnostic biomarkers, motivate the present study. This work develops a machine-learning-based sepsis diagnostic for a high-risk patient group, using a geographically and institutionally diverse collection of nearly 500,000 patient health records. Using only a minimal set of clinical variables, our diagnostics outperform common severity scoring systems and sepsis biomarkers and benefit from being available immediately upon ordering.
Extracted statement — Objective: Sepsis
Research field: Science
Title: Physiological monitoring for critically ill patients: testing a predictive model for the early detection of sepsis
Abstract: OBJECTIVE To assess the predictive value for the early detection of sepsis of the physiological monitoring parameters currently recommended by the Surviving Sepsis Campaign. METHODS The Project IMPACT data set was used to assess whether the physiological parameters of heart rate, mean arterial pressure, body temperature, and respiratory rate can be used to distinguish between critically ill adult patients with and without sepsis in the first 24 hours of admission to an intensive care unit. RESULTS All predictor variables used in the analyses differed significantly between patients with sepsis and patients without sepsis. However, only 2 of the predictor variables, mean arterial pressure and high temperature, were independently associated with sepsis. In addition, the temperature mean for hypothermia was significantly lower in patients without sepsis. The odds ratio for having sepsis was 2.126 for patients with a temperature of 38 degrees C or higher, 3.874 for patients with a mean arterial blood pressure of less than 70 mm Hg, and 4.63 times greater for patients who had both of these conditions. CONCLUSIONS The results support the use of some of the guidelines of the Surviving Sepsis Campaign. However, the lowest mean temperature was significantly less for patients without sepsis than for patients with sepsis, a finding that calls into question the clinical usefulness of using hypothermia as an early predictor of sepsis. Alone the group of variables used is not sufficient for discriminating between critically ill patients with and without sepsis.
Extracted statement — Objective: Sepsis
Research field: Science
Title: Predictive models for severe sepsis in adult ICU patients
Abstract: Intensive Care Unit (ICU) patients have significant morbidity and mortality, often from complications that arise during the hospital stay. Severe sepsis is one of the leading causes of death among these patients. Predictive models have the potential to allow for earlier detection of severe sepsis and ultimately earlier intervention. However, current methods for identifying and predicting severe sepsis are biased and inadequate. The goal of this work is to identify a new framework for the prediction of severe sepsis and identify early predictors utilizing clinical laboratory values and vital signs collected in adult ICU patients. We explore models with logistic regression (LR), support vector machines (SVM), and logistic model trees (LMT) utilizing vital signs, laboratory values, or a combination of vital and laboratory values. When applied to a retrospective cohort of ICU patients, the SVM model using laboratory and vital signs as predictors identified 339 (65%) of the 3,446 patients as developing severe sepsis correctly. Based on this new framework and developed models, we provide a recommendation for the use in clinical decision support in ICU and non-ICU environments.
Extracted statement — Objective: Sepsis
Research field: Science
Title: A Bayesian network for early diagnosis of sepsis patients: a basis for a clinical decision support system
Abstract: Sepsis is a severe medical condition caused by an inordinate immune response to an infection. Early detection of sepsis symptoms is important to prevent the progression into the more severe stages of the disease, which kills one in four it effects. Electronic medical records of 1492 patients containing 233 cases of sepsis were used in a clustering analysis to identify features that are indicative of sepsis and can be further used for training a Bayesian inference network. The Bayesian network was constructed using the systemic inflammatory response syndrome criteria, mean arterial pressure, and lactate levels for sepsis patients. The resulting network reveals a clear correlation between lactate levels and sepsis. Furthermore, it was shown that lactate levels may be predicative of the SIRS criteria. In this light, Bayesian networks of sepsis patients hold the promise of providing a clinical decision support system in the future.
Extracted statement — Objective: Sepsis
Research field: Science
Title: From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system
Abstract: OBJECTIVE To develop a decision support system to identify patients at high risk for hyperlactatemia based upon routinely measured vital signs and laboratory studies. MATERIALS AND METHODS Electronic health records of 741 adult patients at the University of California Davis Health System who met at least two systemic inflammatory response syndrome criteria were used to associate patients' vital signs, white blood cell count (WBC), with sepsis occurrence and mortality. Generative and discriminative classification (naïve Bayes, support vector machines, Gaussian mixture models, hidden Markov models) were used to integrate heterogeneous patient data and form a predictive tool for the inference of lactate level and mortality risk. RESULTS An accuracy of 0.99 and discriminability of 1.00 area under the receiver operating characteristic curve (AUC) for lactate level prediction was obtained when the vital signs and WBC measurements were analysed in a 24 h time bin. An accuracy of 0.73 and discriminability of 0.73 AUC for mortality prediction in patients with sepsis was achieved with only three features: median of lactate levels, mean arterial pressure, and median absolute deviation of the respiratory rate. DISCUSSION This study introduces a new scheme for the prediction of lactate levels and mortality risk from patient vital signs and WBC. Accurate prediction of both these variables can drive the appropriate response by clinical staff and thus may have important implications for patient health and treatment outcome. CONCLUSIONS Effective predictions of lactate levels and mortality risk can be provided with a few clinical variables when the temporal aspect and variability of patient data are considered.
Extracted statement — Objective: Sepsis
Research field: Science
Title: On the Interactions Between Routing and Inventory-Management Policies in a One-WarehouseN-Retailer Distribution System
Abstract: This paper examines the interactions between routing and inventory-management decisions in a two-level supply chain consisting of a cross-docking warehouse and N retailers. Retailer demand is normally distributed and independent across retailers and over time. Travel times are fixed between pairs of system sites. Every m time periods, system inventory is replenished at the warehouse, whereupon an uncapacitated vehicle departs on a route that visits each retailer once and only once, allocating all of its inventory based on the status of inventory at the retailers who have not yet received allocations. The retailers experience newsvendor-type inventory-holding and backorder-penalty costs each period; the vehicle experiences in-transit inventory-holding costs each period. Our goal is to determine a combined system inventory-replenishment, routing, and inventory-allocation policy that minimizes the total expected cost/period of the system over an infinite time horizon. Our analysis begins by examining the determination of the optimal static route, i.e., the best route if the vehicle must travel the same route every replenishment-allocation cycle. Here we demonstrate that the optimal static route is not the shortest-total-distance (TSP) route, but depends on the variance of customer demands, and, if in-transit inventory-holding costs are charged, also on mean customer demands. We then examine dynamic-routing policies, i.e., policies that can change the route from one system-replenishment-allocation cycle to another, based on the status of the retailers' inventories. Here we argue that in the absence of transportation-related cost, the optimal dynamic-routing policy should be viewed as balancing management's ability to respond to system uncertainties (by changing routes) against system uncertainties that are induced by changing routes. We then examine the performance of a change-revert heuristic policy. Although its routing decisions are not fully dynamic, but determined and fixed for a given cycle at the time of each system replenishment, simulation tests with N = 2 and N = 6 retailers indicate that its use can substantially reduce system inventory-related costs even if most of the time the chosen route is the optimal static route.
Extracted statement — approach: Simulation
Research field: Science
Title: Scheduling short-term marine transport of bulk products
Abstract: A multinational company uses a personal computer to schedule a fleet of coastal tankers and barges transporting liquid bulk products among plants, distribution centres (tank farms), and industrial customers. A simple spreadsheet interface cloaks a sophisticated optimization-based decision support system and makes this system useable via a varity of natural languages. The dispatchers, whose native language is not English, and some of whom presumably speak no English at all, communicate via the spreadsheet, and view recommended schedules displayed in Gantt charts both internationally familiar tools. Inside the spreadsheet, a highly detailed simulation can generate every feasible alternate vessel employment schedule, and an integer linear set partitioning model selects one schedule for each vessel so that all loads and deliveries are completed at minimal cost while satisfying all operational requirements. The optimized fleet employment schedule is displyed graphically with hourly time resolution over a planning horizon of 2-3 weeks. Each vessel will customarily make several voyages and many port calls to load and unload products during this time.
Extracted statement — Method: Simulation