New improvements in the Izaña (Tenerife, Spain) global GAW station in-situ greenhouse gases measurement program

Póster presentado en: 16th WMO/IAEA Meeting on Carbon Dioxide, Other Greenhouse Gases, and Related Measurement Techniques celebrado del 25 al 28 de octubre de 2011 en Wellington, Nueva Zelanda

Automation of Patient Trajectory Management: A deep-learning system for critical care outreach

The application of machine learning models to big data has become ubiquitous, however their successful translation into clinical practice is currently mostly limited to the field of imaging. Despite much interest and promise, there are many complex and interrelated barriers that exist in clinical settings, which must be addressed systematically in advance of wide-spread adoption of these technologies. There is limited evidence of comprehensive efforts to consider not only their raw performance metrics, but also their effective deployment, particularly in terms of the ways in which they are perceived, used and accepted by clinicians. The critical care outreach team at St Vincent’s Public Hospital want to automatically prioritise their workload by predicting in-patient deterioration risk, presented as a watch-list application. This work proposes that the proactive management of in-patients at risk of serious deterioration provides a comprehensive case-study in which to understand clinician readiness to adopt deep-learning technology due to the significant known limitations of existing manual processes. Herein is described the development of a proof of concept application uses as its input the subset of real-time clinical data available in the EMR. This data set has the noteworthy challenge of not including any electronically recorded vital signs data. Despite this, the system meets or exceeds similar benchmark models for predicting in-patient death and unplanned ICU admission, using a recurrent neural network architecture, extended with a novel data-augmentation strategy. This augmentation method has been re-implemented in the public MIMIC-III data set to confirm its generalisability. The method is notable for its applicability to discrete time-series data. Furthermore, it is rooted in knowledge of how data entry is performed within the clinical record and is therefore not restricted in applicability to a single clinical domain, instead having the potential for wide-ranging impact. The system was presented to likely end-users to understand their readiness to adopt it into their workflow, using the Technology Adoption Model. In addition to confirming feasibility of predicting risk from this limited data set, this study investigates clinician readiness to adopt artificial intelligence in the critical care setting. This is done with a two-pronged strategy, addressing technical and clinically-focused research questions in parallel

The use of fast initial response features on the homogeneously weighted moving average chart with estimated parameters under the effect of measurement errors

Fast initial response (FIR) features are generally used to improve the sensitivity of memory-type control charts by shrinking time-varying control limits in the earlier stage of the monitoring regime. This paper incorporates FIR features to increase the sensitivity of the homogeneously weighted moving average (HWMA) monitoring schemes with and without measurement errors under constant as well as linearly increasing variance scenarios. The robustness and the performance of the HWMA monitoring schemes are investigated in terms of numerous run-length properties assuming that the underlying process parameters are known and unknown. It is found that the FIR features improves the performance of the HWMA monitoring scheme as compared to the standard no FIR feature HWMA scheme, and at the same time, it is observed that the simultaneous use of a recently proposed FIR feature and multiple measurements significantly reduces the negative effect of measurement errors. An illustrative example on the volume of milk in bottles is used to demonstrate a real-life application.https://wileyonlinelibrary.com/journal/qrehj2022Statistic

Obesity and dental caries in children: Are there more common determinants than diet?

Objective: To examine common risk factors and determinants for overweight/obesity and dental caries in the family setting of children between the ages of 0 and 11 years of age. Methods: A conceptual framework on the social determinants of childhood dental caries and overweight/obesity was developed in this study based on previous literature. It was tested through a qualitative study (Study 1), consisting of semi-structured interviews with parents of obese children in Sheffield and a quantitative study (Study 2) using structural equation modelling (SEM) with data from the Born in Bradford Cohort Study (BIB), dental general anaesthetics (GA) and data from the oral health survey of 5-year-old children 2014/2015 of the same population. Results: Study 1: 13 parents participated in the interviews with a total of 15 children. 8/15 children had previous experience of dental caries. All children were classified as obese. Parents highlighted a diet high in sugar affecting dental caries and overweight/obesity in children. In addition, weather and neighbourhood safety were mentioned as important factors related to physical activity and therefore overweight/obesity prevention. Study 2: 171 children were included in the analysis, 136/171 (GA treatment), 35/171 (oral health survey of five-year-old children 2014-15) with an average dmft of 9.1 and 0.9 respectively. 23.4% of all children were overweight/obese. 46.2% of the sample were male. Six determinants were found to be significant for both childhood dental caries and overweight/obesity: frequency of drinking sugar-sweetened beverages, sex, emotional and behavioural well-being of the child, level of deprivation, caregivers feeding style, and maternal alcohol consumption. Conclusion: Six common risk factors and determinants for childhood dental caries and overweight/obesity were identified. Parents of obese children confirmed the influence of a high sugary diet on childhood dental caries and overweight/obesity