Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: A video game improves behavioral outcomes in adolescents and young adults with cancer: A randomized trial
Abstract: OBJECTIVE. Suboptimal adherence to self-administered medications is a common problem. The purpose of this study was to determine the effectiveness of a video-game intervention for improving adherence and other behavioral outcomes for adolescents and young adults with malignancies including acute leukemia, lymphoma, and soft-tissue sarcoma. METHODS. A randomized trial with baseline and 1- and 3-month assessments was conducted from 2004 to 2005 at 34 medical centers in the United States, Canada, and Australia. A total of 375 male and female patients who were 13 to 29 years old, had an initial or relapse diagnosis of a malignancy, and currently undergoing treatment and expected to continue treatment for at least 4 months from baseline assessment were randomly assigned to the intervention or control group. The intervention was a video game that addressed issues of cancer treatment and care for teenagers and young adults. Outcome measures included adherence, self-efficacy, knowledge, control, stress, and quality of life. For patients who were prescribed prophylactic antibiotics, adherence to trimethoprim-sulfamethoxazole was tracked by electronic pill-monitoring devices (n = 200). Adherence to 6-mercaptopurine was assessed through serum metabolite assays (n = 54). RESULTS. Adherence to trimethoprim-sulfamethoxazole and 6-mercaptopurine was greater in the intervention group. Self-efficacy and knowledge also increased in the intervention group compared with the control group. The intervention did not affect self-report measures of adherence, stress, control, or quality of life. CONCLUSIONS. The video-game intervention significantly improved treatment adherence and indicators of cancer-related self-efficacy and knowledge in adolescents and young adults who were undergoing cancer therapy. The findings support current efforts to develop effective video-game interventions for education and training in health care.
Extracted statement — Topic: Cancer treatment
Research field: Science
Title: The evacuation optimal network design problem: model formulation and comparisons
Abstract: Abstract The goal of this paper is twofold. First, we present a stochastic programming-based model that provides optimal design solutions for transportation networks in light of possible emergency evacuations. Second, as traffic congestion is a growing problem in metropolitan areas around the world, decision makers might not be willing to design transportation networks solely for evacuation purposes since daily traffic patterns differ tremendously from traffic observed during evacuations. This is especially true when potential disaster locations are limited in number and confined to specific regions (e.g. coastal regions might be more prone to flooding). However, as extreme events such as excessive rainfall become more prevalent everywhere, it is less obvious that the design of transportation networks for evacuation planning and congestion reduction is mutually exclusive. That is, capacity expansion decisions to reduce congestion might also be reasonable from an evacuation planning point of view. Conversely, expansion decisions for evacuation planning might turn out to be effective for congestion relief. To date, no numerical evidence has been presented in the literature to support or disprove these conjectures. Preliminary numerical evidence is provided in this paper.
Extracted statement — Decisions First-stage: Capacity expansion
Research field: Science
Title: Deal or No Deal: using games to improve student learning, retention and decision-making
Abstract: Student understanding and retention can be enhanced and improved by providing alternative learning activities and environments. Education theory recognizes the value of incorporating alternative activities (games, exercises and simulations) to stimulate student interest in the educational environment, enhance transfer of knowledge and improve learned retention with meaningful repetition. In this case study, we investigate using an online version of the television game show, ‘Deal or No Deal’, to enhance student understanding and retention by playing the game to learn expected value in an introductory statistics course, and to foster development of critical thinking skills necessary to succeed in the modern business environment. Enhancing the thinking process of problem solving using repetitive games should also improve a student's ability to follow non-mathematical problem-solving processes, which should improve the overall ability to process information and make logical decisions. Learning and retention are measured to evaluate the success of the students’ performance.
Extracted statement — Method: Case Study
Research field: Science
Title: Artificial neural networks to infer biomass and product concentration during the production of penicillin G acylase from Bacillus megaterium
Abstract: BACKGROUND: Production of microbial enzymes in bioreactors is a complex process including such phenomena as metabolic networks and mass transport resistances. The use of neural networks (NNs) to infer the state of bioreactors may be an interesting option that may handle the nonlinear dynamics of biomass growth and protein production. RESULTS: Feedforward multilayer perceptron (MLP) NNs were used for identification of the cultivation phase of Bacillus megaterium to produce the enzyme penicillin G acylase (EC. 3.5.1.11). The following variables were used as input to the net: run time and carbon dioxide concentration in the exhausted gas. The NN output associates a numerical value to the metabolic state of the cultivation, close to 0 during the lag phase, close to 1 during the exponential phase and approximately 2 for the stationary phase. This is a non-conventional approach for pattern recognition. During the exponential phase, another MLP was used to infer cellular concentration. Time, carbon dioxide concentration and stirrer speed form an integrated net input vector. Cellular concentrations provided by the NN were used in a hybrid approach to estimate product concentrations of the enzyme. The model employed a first-order approximation. CONCLUSION: Results showed that the algorithm was able to infer accurate values of cellular and product concentrations up to the end of the exponential growth phase, where an industrial run should stop. Copyright © 2008 Society of Chemical Industry
Extracted statement — Objective/estimate(s) process systems: Cellular concentration
Research field: Science
Title: Identifying the factors influencing the performance of reverse supply chains (RSC)
Abstract: This paper aims to extract the factors influencing the performance of reverse supply chains (RSCs) based on the structure equation model (SEM). We first introduce the definition of RSC and describe its current status and follow this with a literature review of previous RSC studies and the technology acceptance model . We next develop our research model and 11 hypotheses and then use SEM to test our model and identify those factors that actually influence the success of RSC. Next, we use both questionnaire and web‐based methods to survey five companies which have RSC operation experience in China and Korea. Using the 168 responses, we used measurement modeling test and SEM to validate our proposed hypotheses. As a result, nine hypotheses were accepted while two were rejected. We found that ease of use, perceived usefulness, service quality, channel relationship and RSC cost were the five most important factors which influence the success of RSC. Finally, we conclude by highlighting our research contribution and propose future research.
Extracted statement — Critical success factors: channel relationship
Research field: Science
Title: Characterization of Two Chitinase Genes and One Chitosanase Gene Encoded by Chlorella Virus PBCV-1
Abstract: Chlorella virus PBCV-1 encodes two putative chitinase genes, a181/182r and a260r, and one chitosanase gene, a292l. The three genes were cloned and expressed in Escherichia coli. The recombinant A181/182R protein has endochitinase activity, recombinant A260R has both endochitinase and exochitinase activities, and recombinant A292L has chitosanase activity. Transcription of a181/182r, a260r, and a292l genes begins at 30, 60, and 60 min p.i., respectively; transcription of all three genes continues until the cells lyse. A181/182R, A260R, and A292L proteins are first detected by Western blots at 60, 90, and 120 min p.i., respectively. Therefore, a181/182r is an early gene and a260r and a292l are late genes. All three genes are widespread in chlorella viruses. Phylogenetic analyses indicate that the ancestral condition of the a181/182r gene arose from the most recent common ancestor of a gene found in tobacco, whereas the genealogical position of the a260r gene could not be unambiguously resolved.
Extracted statement — Sources: Chlorella virus
Research field: Science
Title: Electronic health record-based detection of risk factors for Clostridium difficile infection relapse
Abstract: Objective. A major challenge in treating Clostridium difficile infection (CDI) is relapse. Many new therapies are being developed to help prevent this outcome. We sought to establish risk factors for relapse and determine whether fields available in an electronic health record (EHR) could be used to identify high-risk patients for targeted relapse prevention strategies. Design. Retrospective cohort study. Setting. Large clinical data warehouse at a 4-hospital healthcare organization. Participants. Data were gathered from January 2006 through October 2010. Subjects were all inpatient episodes of a positive C. difficile test where patients were available for 56 days of follow-up. Methods. Relapse was defined as another positive test between 15 and 56 days after the initial test. Multivariable regression was performed to identify factors independently associated with CDI relapse. Results. Eight hundred twenty-nine episodes met eligibility criteria, and 198 resulted in relapse (23.9%). In the final multivariable analysis, risk of relapse was associated with age (odds ratio [OR], 1.02 per year [95% confidence interval (CI), 1.01–1.03]), fluoroquinolone exposure in the 90 days before diagnosis (OR, 1.58 [95% CI, 1.11–2.26]), intensive care unit stay in the 30 days before diagnosis (OR, 0.47 [95% CI, 0.30–0.75]), cephalosporin (OR, 1.80 [95% CI, 1.19–2.71]), proton pump inhibitor (PPI; OR, 1.55 [95% CI, 1.05–2.29]), and metronidazole exposure after diagnosis (OR, 2.74 [95% CI, 1.64–4.60]). A prediction model tuned to ensure a 50% probability of relapse would flag 14.6% of CDI episodes. Conclusions. Data from a comprehensive EHR can be used to identify patients at high risk for CDI relapse. Major risk factors include antibiotic and PPI exposure.
Extracted statement — Features: clinical data
Research field: Science
Title: Development and validation of a Clostridium difficile infection risk prediction model
Abstract: Objective. To develop and validate a risk prediction model that could identify patients at high risk for Clostridium difficile infection (CDI) before they develop disease. Design and Setting. Retrospective cohort study in a tertiary care medical center. Patients. Patients admitted to the hospital for at least 48 hours during the calendar year 2003. Methods. Data were collected electronically from the hospital's Medical Informatics database and analyzed with logistic regression to determine variables that best predicted patients' risk for development of CDI. Model discrimination and calibration were calculated. The model was bootstrapped 500 times to validate the predictive accuracy. A receiver operating characteristic curve was calculated to evaluate potential risk cutoffs. Results. A total of 35,350 admitted patients, including 329 with CDI, were studied. Variables in the risk prediction model were age, CDI pressure, times admitted to hospital in the previous 60 days, modified Acute Physiology Score, days of treatment with high-risk antibiotics, whether albumin level was low, admission to an intensive care unit, and receipt of laxatives, gastric acid suppressors, or antimotility drugs. The calibration and discrimination of the model were very good to excellent (C index, 0.88; Brier score, 0.009). Conclusions. The CDI risk prediction model performed well. Further study is needed to determine whether it could be used in a clinical setting to prevent CDI-associated outcomes and reduce costs.
Extracted statement — Infection: Clostridium difficile infection
Research field: Science
Title: Prediction of Recurrent Clostridium Difficile Infection Using Comprehensive Electronic Medical Records in an Integrated Healthcare Delivery System
Abstract: BACKGROUND Predicting recurrent Clostridium difficile infection (rCDI) remains difficult. METHODS. We employed a retrospective cohort design. Granular electronic medical record (EMR) data had been collected from patients hospitalized at 21 Kaiser Permanente Northern California hospitals. The derivation dataset (2007–2013) included data from 9,386 patients who experienced incident CDI (iCDI) and 1,311 who experienced their first CDI recurrences (rCDI). The validation dataset (2014) included data from 1,865 patients who experienced incident CDI and 144 who experienced rCDI. Using multiple techniques, including machine learning, we evaluated more than 150 potential predictors. Our final analyses evaluated 3 models with varying degrees of complexity and 1 previously published model. RESULTS Despite having a large multicenter cohort and access to granular EMR data (eg, vital signs, and laboratory test results), none of the models discriminated well (c statistics, 0.591–0.605), had good calibration, or had good explanatory power. CONCLUSIONS Our ability to predict rCDI remains limited. Given currently available EMR technology, improvements in prediction will require incorporating new variables because currently available data elements lack adequate explanatory power. Infect Control Hosp Epidemiol 2017;38:1196–1203
Extracted statement — Infection: Clostridium difficile infection
Research field: Science
Title: Electronic health record-based detection of risk factors for Clostridium difficile infection relapse
Abstract: Objective. A major challenge in treating Clostridium difficile infection (CDI) is relapse. Many new therapies are being developed to help prevent this outcome. We sought to establish risk factors for relapse and determine whether fields available in an electronic health record (EHR) could be used to identify high-risk patients for targeted relapse prevention strategies. Design. Retrospective cohort study. Setting. Large clinical data warehouse at a 4-hospital healthcare organization. Participants. Data were gathered from January 2006 through October 2010. Subjects were all inpatient episodes of a positive C. difficile test where patients were available for 56 days of follow-up. Methods. Relapse was defined as another positive test between 15 and 56 days after the initial test. Multivariable regression was performed to identify factors independently associated with CDI relapse. Results. Eight hundred twenty-nine episodes met eligibility criteria, and 198 resulted in relapse (23.9%). In the final multivariable analysis, risk of relapse was associated with age (odds ratio [OR], 1.02 per year [95% confidence interval (CI), 1.01–1.03]), fluoroquinolone exposure in the 90 days before diagnosis (OR, 1.58 [95% CI, 1.11–2.26]), intensive care unit stay in the 30 days before diagnosis (OR, 0.47 [95% CI, 0.30–0.75]), cephalosporin (OR, 1.80 [95% CI, 1.19–2.71]), proton pump inhibitor (PPI; OR, 1.55 [95% CI, 1.05–2.29]), and metronidazole exposure after diagnosis (OR, 2.74 [95% CI, 1.64–4.60]). A prediction model tuned to ensure a 50% probability of relapse would flag 14.6% of CDI episodes. Conclusions. Data from a comprehensive EHR can be used to identify patients at high risk for CDI relapse. Major risk factors include antibiotic and PPI exposure.
Extracted statement — Infection: Clostridium difficile infection
Research field: Science
Title: Improving Risk Prediction of Clostridium Difficile Infection Using Temporal Event-Pairs
Abstract: Clostridium Difficile Infection (CDI) is a contagious healthcare-associated infection that imposes a significant burden on the healthcare system. In 2011 alone, half a million patients suffered from CDI in the United States, 29,000 dying within 30 days of diagnosis. Determining which hospital patients are at risk for developing CDI is critical to helping healthcare workers take timely measures to prevent or detect and treat this infection. We improve the state of the art of CDI risk prediction by designing an ensemble logistic regression classifier that given partial patient visit histories, outputs the risk of patients acquiring CDI during their current hospital visit. The novelty of our approach lies in the representation of each patient visit as a collection of co-occurring and chronologically ordered pairs of events. This choice is motivated by our hypothesis that CDI risk is influenced not just by individual events (e.g., Being prescribed a first generation cephalosporin antibiotic), but by the temporal ordering of individual events (e.g., Antibiotic prescription followed by transfer to a certain hospital unit). While this choice explodes the number of features, we use a randomized greedy feature selection algorithm followed by BIC minimization to reduce the dimensionality of the feature space, while retaining the most relevant features. We apply our approach to a rich dataset from the University of Iowa Hospitals and Clinics (UIHC), curated from diverse sources, consisting of 200,000 visits (30,000 per year, 2006-2011) involving 125,000 unique patients, 2 million diagnoses, 8 million prescriptions, 400,000 room transfers spanning a hospital with 700 patient rooms and 200 units. Our approach to classification produces better risk predictions (AUC) than existing risk estimators for CDI, even when trained just on data available at patient admission. It also identifies novel risk factors for CDI that are combinations of co-occurring and chronologically ordered events.
Extracted statement — Infection: Clostridium difficile infection
Research field: Science
Title: A Multi-Center Prospective Derivation and Validation of a Clinical Prediction Tool for Severe Clostridium difficile Infection
Abstract: Background and Aims Prediction of severe clinical outcomes in Clostridium difficile infection (CDI) is important to inform management decisions for optimum patient care. Currently, treatment recommendations for CDI vary based on disease severity but validated methods to predict severe disease are lacking. The aim of the study was to derive and validate a clinical prediction tool for severe outcomes in CDI. Methods A cohort totaling 638 patients with CDI was prospectively studied at three tertiary care clinical sites (Boston, Dublin and Houston). The clinical prediction rule (CPR) was developed by multivariate logistic regression analysis using the Boston cohort and the performance of this model was then evaluated in the combined Houston and Dublin cohorts. Results The CPR included the following three binary variables: age ≥ 65 years, peak serum creatinine ≥2 mg/dL and peak peripheral blood leukocyte count of ≥20,000 cells/μL. The Clostridium difficile severity score (CDSS) correctly classified 76.5% (95% CI: 70.87-81.31) and 72.5% (95% CI: 67.52-76.91) of patients in the derivation and validation cohorts, respectively. In the validation cohort, CDSS scores of 0, 1, 2 or 3 were associated with severe clinical outcomes of CDI in 4.7%, 13.8%, 33.3% and 40.0% of cases respectively. Conclusions We prospectively derived and validated a clinical prediction rule for severe CDI that is simple, reliable and accurate and can be used to identify high-risk patients most likely to benefit from measures to prevent complications of CDI.
Extracted statement — Infection: Clostridium difficile infection
Research field: Science
Title: A Generalizable, Data-Driven Approach to Predict Daily Risk of Clostridium difficile Infection at Two Large Academic Health Centers
Abstract: OBJECTIVE An estimated 293,300 healthcare-associated cases of Clostridium difficile infection (CDI) occur annually in the United States. To date, research has focused on developing risk prediction models for CDI that work well across institutions. However, this one-size-fits-all approach ignores important hospital-specific factors. We focus on a generalizable method for building facility-specific models. We demonstrate the applicability of the approach using electronic health records (EHR) from the University of Michigan Hospitals (UM) and the Massachusetts General Hospital (MGH). METHODS We utilized EHR data from 191,014 adult admissions to UM and 65,718 adult admissions to MGH. We extracted patient demographics, admission details, patient history, and daily hospitalization details, resulting in 4,836 features from patients at UM and 1,837 from patients at MGH. We used L2 regularized logistic regression to learn the models, and we measured the discriminative performance of the models on held-out data from each hospital. RESULTS Using the UM and MGH test data, the models achieved area under the receiver operating characteristic curve (AUROC) values of 0.82 (95% confidence interval [CI], 0.80–0.84) and 0.75 ( 95% CI, 0.73–0.78), respectively. Some predictive factors were shared between the 2 models, but many of the top predictive factors differed between facilities. CONCLUSION A data-driven approach to building models for estimating daily patient risk for CDI was used to build institution-specific models at 2 large hospitals with different patient populations and EHR systems. In contrast to traditional approaches that focus on developing models that apply across hospitals, our generalizable approach yields risk-stratification models tailored to an institution. These hospital-specific models allow for earlier and more accurate identification of high-risk patients and better targeting of infection prevention strategies. Infect Control Hosp Epidemiol 2018;39:425–433
Extracted statement — Infection: Clostridium difficile infection
Research field: Science
Title: Cloud ERP system customization challenges
Abstract: Customization is one of the known challenges in traditional ERP systems. With the advent of Cloud ERP systems, a question of determining the state of such systems regarding customization and configuration ability arises. As there are only a few literature sources partially covering this topic, a more comprehensive and systematic literature review is needed. Thus, this paper presents a literature review performed in order to give an overview of reported research on "Cloud ERP Customization" topic performed in the last 5 years. In two search iterations, a total of 32 relevant papers are identified and analyzed. The results show that several dominant research trends are identified along with 12 challenges and issues. Additionally, based on the results, the possible future researches are proposed.
Extracted statement — field: Cloud ERP