Time, timing, talking and training : findings from an exploratory action research study to improve quality of end of life care for minority ethnic kidney patients

Background. With an ageing and increasingly diverse population at risk from rising levels of obesity, diabetes and cardiovascular disease, including kidney complications, there is a need to provide quality care at all stages in the care pathway including at the end of life and to all patients. Aim. This study purposively explored South Asian patients' experiences of kidney end of life care to understand how services can be delivered in a way that meets diverse patient needs. Methods. Within an action research design 14 focus groups (45 care providers) of kidney care providers discussed the recruitment and analysis of individual interviews with 16 South Asian kidney patients (eight men, eight women). Emergent themes from the focus groups were analysed thematically. The research took place at four UK centres providing kidney care to diverse populations: West London, Luton, Leicester and Bradford. Results. Key themes related to time and the timing of discussions about end of life care and the factors that place limitations on patients and providers in talking about end of life care. Lack of time and confidence of nurses in areas of kidney care, individual attitudes and workforce composition influence whether and how patients have access to end of life care through kidney services. Conclusion. Training, team work and time to discuss overarching issues (including timing and communication about end of life) with colleagues could support service providers to facilitate access and delivery of end of life care to this group of patients.Peer reviewedFinal Published versio

Exploring access to end of life care for ethnic minorities with end stage kidney disease through recruitment in action research

BACKGROUND: Variation in provision of palliative care in kidney services and practitioner concerns to provide equitable access led to the development of this study which focussed on the perspectives of South Asian patients and their care providers. As people with a South Asian background experience a higher risk of Type 2 Diabetes (T2DM) and end stage kidney failure (ESKF) compared to the majority population but wait longer for a transplant, there is a need for end of life care to be accessible for this group of patients. Furthermore because non English speakers and people at end of life are often excluded from research there is a dearth of research evidence with which to inform service improvement. This paper aims to explore issues relating to the process of recruitment of patients for a research project which contribute to our understanding of access to end of life care for ethnic minority patients in the kidney setting. METHODS: The study employed an action research methodology with interviews and focus groups to capture and reflect on the process of engaging with South Asian patients about end of life care. Researchers and kidney care clinicians on four NHS sites in the UK recruited South Asian patients with ESKF who were requiring end of life care to take part in individual interviews; and other clinicians who provided care to South Asian kidney patients at end of life to take part in focus groups exploring end of life care issues. In action research planning, action and evaluation are interlinked and data were analysed with emergent themes fed back to care providers through the research cycle. Reflections on the process of patient recruitment generated focus group discussions about access which were analysed thematically and reported here. RESULTS: Sixteen patients were recruited to interview and 45 different care providers took part in 14 focus groups across the sites. The process of recruiting patients to interview and subsequent focus group data highlighted some of the key issues concerning access to end of life care. These were: the identification of patients approaching end of life; and their awareness of end of life care; language barriers and informal carers' roles in mediating communication; and contrasting cultures in end of life kidney care. CONCLUSIONS: Reflection on the process of recruitment in this action research study provided insight into the complex scenario of end of life in kidney care. Some of the emerging issues such as the difficulty identifying patients are likely to be common across all patient groups, whilst others concerning language barriers and third party communication are more specific to ethnic minorities. A focus on South Asian ethnicity contributes to better understanding of patient perspectives and generic concepts as well as access to end of life kidney care for this group of patients in the UK. Action research was a useful methodology for achieving this and for informing future research to include informal carers and other ethnic groups.Peer reviewedFinal Published versio

Genetic programming and serial processing for time series classification

This work describes an approach devised by the authors for time series classification. In our approach genetic programming is used in combination with a serial processing of data, where the last output is the result of the classification. The use of genetic programming for classification, although still a field where more research in needed, is not new. However, the application of genetic programming to classification tasks is normally done by considering the input data as a feature vector. That is, to the best of our knowledge, there are not examples in the genetic programming literature of approaches where the time series data are processed serially and the last output is considered as the classification result. The serial processing approach presented here fills a gap in the existing literature. This approach was tested in three different problems. Two of them are real world problems whose data were gathered for online or conference competitions. As there are published results of these two problems this gives us the chance to compare the performance of our approach against top performing methods. The serial processing of data in combination with genetic programming obtained competitive results in both competitions, showing its potential for solving time series classification problems. The main advantage of our serial processing approach is that it can easily handle very large datasets.Alfaro Cid, E.; Sharman, KC.; Esparcia Alcázar, AI. (2014). Genetic programming and serial processing for time series classification. Evolutionary Computation. 22(2):265-285. doi:10.1162/EVCO_a_00110S265285222Adeodato, P. J. L., Arnaud, A. L., Vasconcelos, G. C., Cunha, R. C. L. V., Gurgel, T. B., & Monteiro, D. S. M. P. (2009). 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R., … Birbaumer, N. (2004). The BCI Competition 2003: Progress and Perspectives in Detection and Discrimination of EEG Single Trials. IEEE Transactions on Biomedical Engineering, 51(6), 1044-1051. doi:10.1109/tbme.2004.826692Borrelli, A., De Falco, I., Della Cioppa, A., Nicodemi, M., & Trautteur, G. (2006). Performance of genetic programming to extract the trend in noisy data series. Physica A: Statistical Mechanics and its Applications, 370(1), 104-108. doi:10.1016/j.physa.2006.04.025Eads, D. R., Hill, D., Davis, S., Perkins, S. J., Ma, J., Porter, R. B., & Theiler, J. P. (2002). Genetic Algorithms and Support Vector Machines for Time Series Classification. Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V. doi:10.1117/12.453526Eggermont, J., Eiben, A. E., & van Hemert, J. I. (1999). A Comparison of Genetic Programming Variants for Data Classification. Lecture Notes in Computer Science, 281-290. doi:10.1007/3-540-48412-4_24Holladay, K. 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Reducing overfitting in genetic programming models for software quality classification. Eighth IEEE International Symposium on High Assurance Systems Engineering, 2004. Proceedings. doi:10.1109/hase.2004.1281730Luke, S. (2000). Two fast tree-creation algorithms for genetic programming. IEEE Transactions on Evolutionary Computation, 4(3), 274-283. doi:10.1109/4235.873237Luke, S., & Panait, L. (2006). A Comparison of Bloat Control Methods for Genetic Programming. Evolutionary Computation, 14(3), 309-344. doi:10.1162/evco.2006.14.3.309Mensh, B. D., Werfel, J., & Seung, H. S. (2004). BCI Competition 2003—Data Set Ia: Combining Gamma-Band Power With Slow Cortical Potentials to Improve Single-Trial Classification of Electroencephalographic Signals. IEEE Transactions on Biomedical Engineering, 51(6), 1052-1056. doi:10.1109/tbme.2004.827081Muni, D. P., Pal, N. R., & Das, J. (2006). Genetic programming for simultaneous feature selection and classifier design. 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Performance Optimization in Control Systems Using Entropy

[Resumen] Calculamos, analizamos y medimos la entropía relativa en sistemas de control. Los datos obtenidos se pueden utilizar para detectar errores en su funcionamiento o para ajustar los recursos de cálculo de los componentes, mejorando su rendimiento. Evaluamos la ventaja del método a través de un experimento de simulación.[Abstract] We calculate, analyze and measure the relative entropy in control systems. The data obtained can be used to detect a source of malfunction or to adjust the computation resource of the components improving its performance. We evaluate the advantage of the method through a simulation experiment.Este trabajo ha sido financiado por el proyecto CPS4EU de la Comisión Europea (ECSEL-JU, contract 826276)https://doi.org/10.17979/spudc.978849749804