A predictive phenomenological tool at small Bjorken-x

We present the results from global fits of inclusive DIS experimental data using the Balitsky-Kovchegov equation with running coupling.Comment: 5 pages, 2 figures, prepared for the Proceedings of 'Hot Quarks 2010

LIFE3: A predictive costing tool for digital collections

Predicting the costs of long-term digital preservation is a crucial yet complex task for even the largest repositories and institutions. For smaller projects and individual researchers faced with preservation requirements, the problem is even more overwhelming, as they lack the accumulated experience of the former. Yet being able to estimate future preservation costs is vital to answering a range of important questions for each. The LIFE (Life Cycle Information for E-Literature) project, which has just completed its third phase, helps institutions and researchers address these concerns, reducing the financial and preservation risks, and allowing decision makers to assess a range of options in order to achieve effective preservation while operating within financial restraints. The project is a collaboration between University College London (UCL), The British Library and the Humanities Advanced Technology and Information Institute (HATII) at the University of Glasgow. Funding has been supplied in the UK by the Joint Information Systems Committee (JISC) and the Research Information Network (RIN)

Mercury: using the QuPreSS reference model to evaluate predictive services

Nowadays, lots of service providers offer predictive services that show in advance a condition or occurrence about the future. As a consequence, it becomes necessary for service customers to select the predictive service that best satisfies their needs. The QuPreSS reference model provides a standard solution for the selection of predictive services based on the quality of their predictions. QuPreSS has been designed to be applicable in any predictive domain (e.g., weather forecasting, economics, and medicine). This paper presents Mercury, a tool based on the QuPreSS reference model and customized to the weather forecast domain. Mercury measures weather predictive services' quality, and automates the context-dependent selection of the most accurate predictive service to satisfy a customer query. To do so, candidate predictive services are monitored so that their predictions can be eventually compared to real observations obtained from a trusted source. Mercury is a proof-of-concept of QuPreSS that aims to show that the selection of predictive services can be driven by the quality of their predictions. Throughout the paper, we show how Mercury was built from the QuPreSS reference model and how it can be installed and used.Peer ReviewedPostprint (author's final draft

Predictive validity of the START for unauthorised leave and substance abuse in a secure mental health setting:a pseudo-prospective cohort study

Background Risk assessment and management is central to the nursing role in forensic mental health settings. The Short Term Assessment of Risk and Treatability (START) aims to support assessment through identification of risk and protective factors. It has demonstrated predictive validity for aggression; it also aims to aid risk assessment for unauthorised leave and substance abuse where its performance is relatively untested. Objectives To test the predictive validity of the START for unauthorised leave and substance abuse. Design A naturalistic, pseudo-prospective cohort study. Settings Four centres of a large UK provider of secure inpatient mental health services. Participants Inpatients resident between May 2011 and October 2013 who remained in the service for 3-months following assessment with the START by their clinical team. Exclusion criteria were missing assessment data in excess of prorating guidelines. Of 900 eligible patients 73 were excluded leaving a final sample size of n = 827 (response rate 91.9%). Mean age was 38.5 years (SD = 16.7); most participants (72.2%) were male; common diagnoses were schizophrenia-type disorders, personality disorders, organic disorders, developmental disorders and intellectual disability. Methods Routinely conducted START assessments were gathered. Subsequent incidents of substance abuse and unauthorised leave were coded independently. Positive and negative predictive values of low and elevated risk were calculated. Receiver Operating Characteristic analysis was conducted to ascertain the predictive accuracy of the assessments based on their sensitivity and specificity. Results Patient-based rates of unauthorised leave (2.4%) and substance abuse (1.6%) were low. The positive and negative predictive values for unauthorised leave were 5.9% and 98.4%; and for substance abuse 8.1% and 99.0%. The START specific risk estimate for unauthorised leave predicted its associated outcome (Area under the curve = .659, p < .05, 95% CI .531, .786); the substance abuse risk estimate predicted its outcome with a large effect size (Area under the curve = .723, p < .01, 95% CI .568, .879). Conclusions The study provides limited support for the START by demonstrating the predictive validity of its specific risk estimates for substance abuse and unauthorised leave. High negative predictive values suggest the tool may be of most utility in screening out low risk individuals from unnecessary restrictive interventions; very low positive predictive values suggest caution before implementing restrictive interventions in those rated at elevated risk. Researchers should investigate how multidisciplinary teams formulate risk assessments for these outcomes since they outperform the quantitative element of this tool

Automated Identification of Unhealthy Drinking Using Routinely Collected Data: A Machine Learning Approach

Background: Unhealthy drinking is prevalent in the United States and can lead to serious health and social consequences, yet it is under-diagnosed and under-treated. Identifying unhealthy drinkers can be time-consuming for primary care providers. An automated tool for identification would allow attention to be focused on patients most likely to need care and therefore increase efficiency and effectiveness. Objectives: To build a clinical prediction tool for unhealthy drinking based solely on routinely collected demographic and laboratory data. Methods: We obtained demographic and laboratory data on 89,325 adults seen at the University of Vermont Medical Center from 2011-2017. Logistic regression, support vector machines (SVM), k-nearest neighbor, and random forests were each used to build clinical prediction models. The model with the largest area under the receiver operator curve (AUC) was selected. Results: SVM with polynomials of degree 3 produced the largest AUC. The most influential predictors were alkaline phosphatase, gender, glucose, and serum bicarbonate. The optimum operating point had sensitivity 31.1%, specificity 91.2%, positive predictive value 50.4%, and negative predictive value 82.1%. Application of the tool increased the prevalence of unhealthy drinking from 18.3% to 32.4%, while reducing the target population by 22%. Limitations: Universal screening was not used during the time data was collected. The prevalence of unhealthy drinking among those screened was 60% suggesting the AUDIT-C was administered to confirm rather than screen for unhealthy drinking. Conclusion: An automated tool, using commonly available data, can identify a subset of patients who appear to warrant clinical attention for unhealthy drinking

A predictive phenomenological tool at small Bjorken-x

We present the results from global fits of inclusive DIS experimental data using the Balitsky-Kovchegov equation with running coupling.Comment: 5 pages, 2 figures, prepared for the Proceedings of 'Hot Quarks 2010