DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls

Advances in network programmability enable operators to 'slice' the physical infrastructure into independent logical networks. By this approach, each network slice aims to accommodate the demands of increasingly diverse services. However, precise allocation of resources to slices across future 5G millimetre-wave backhaul networks, to optimise the total network utility, is challenging. This is because the performance of different services often depends on conflicting requirements, including bandwidth, sensitivity to delay, or the monetary value of the traffic incurred. In this paper, we put forward a general rate utility framework for slicing mm-wave backhaul links, encompassing all known types of service utilities, i.e. logarithmic, sigmoid, polynomial, and linear. We then introduce DELMU, a deep learning solution that tackles the complexity of optimising non-convex objective functions built upon arbitrary combinations of such utilities. Specifically, by employing a stack of convolutional blocks, DELMU can learn correlations between traffic demands and achievable optimal rate assignments. We further regulate the inferences made by the neural network through a simple 'sanity check' routine, which guarantees both flow rate admissibility within the network's capacity region and minimum service levels. The proposed method can be trained within minutes, following which it computes rate allocations that match those obtained with state-of-the-art global optimisation algorithms, yet orders of magnitude faster. This confirms the applicability of DELMU to highly dynamic traffic regimes and we demonstrate up to 62% network utility gains over a baseline greedy approach.Comment: remove LaTeX remains in abstract; change the font for acrony

Challenges to the provision of diabetes care in first nations communities: results from a national survey of healthcare providers in Canada

<p>Abstract</p> <p>Background</p> <p>Aboriginal peoples globally, and First Nations peoples in Canada particularly, suffer from high rates of type 2 diabetes and related complications compared with the general population. Research into the unique barriers faced by healthcare providers working in on-reserve First Nations communities is essential for developing effective quality improvement strategies.</p> <p>Methods</p> <p>In Phase I of this two-phased study, semi-structured interviews and focus groups were held with 24 healthcare providers in the Sioux Lookout Zone in north-western Ontario. A follow-up survey was conducted in Phase II as part of a larger project, the Canadian First Nations Diabetes Clinical Management and Epidemiologic (CIRCLE) study. The survey was completed with 244 healthcare providers in 19 First Nations communities in 7 Canadian provinces, representing three isolation levels (isolated, semi-isolated, non-isolated). Interviews, focus groups and survey questions all related to barriers to providing optimal diabetes care in First Nations communities.</p> <p>Results</p> <p>the key factors emerging from interviews and focus group discussions were at the patient, provider, and systemic level. Survey results indicated that, across three isolation levels, healthcare providers' perceived patient factors as having the largest impact on diabetes care. However, physicians and nurses were more likely to rank patient factors as having a large impact on care than community health representatives (CHRs) and physicians were significantly less likely to rank patient-provider communication as having a large impact than CHRs.</p> <p>Conclusions</p> <p>Addressing patient factors was considered the highest impact strategy for improving diabetes care. While this may reflect "patient blaming," it also suggests that self-management strategies may be well-suited for this context. Program planning should focus on training programs for CHRs, who provide a unique link between patients and clinical services. Research incorporating patient perspectives is needed to complete this picture and inform quality improvement initiatives.</p

Optimisation of Perioperative Cardiovascular Management to Improve Surgical Outcome II (OPTIMISE II) trial: study protocol for a multicentre international trial of cardiac output-guided fluid therapy with low-dose inotrope infusion compared with usual care in patients undergoing major elective gastrointestinal surgery.

INTRODUCTION: Postoperative morbidity and mortality in older patients with comorbidities undergoing gastrointestinal surgery are a major burden on healthcare systems. Infections after surgery are common in such patients, prolonging hospitalisation and reducing postoperative short-term and long-term survival. Optimal management of perioperative intravenous fluids and inotropic drugs may reduce infection rates and improve outcomes from surgery. Previous small trials of cardiac-output-guided haemodynamic therapy algorithms suggested a modest reduction in postoperative morbidity. A large definitive trial is needed to confirm or refute this and inform widespread clinical practice. METHODS: The Optimisation of Perioperative Cardiovascular Management to Improve Surgical Outcome II (OPTIMISE II) trial is a multicentre, international, parallel group, open, randomised controlled trial. 2502 high-risk patients undergoing major elective gastrointestinal surgery will be randomly allocated in a 1:1 ratio using minimisation to minimally invasive cardiac output monitoring to guide protocolised administration of intravenous fluid combined with low-dose inotrope infusion, or usual care. The trial intervention will be carried out during and for 4 hours after surgery. The primary outcome is postoperative infection of Clavien-Dindo grade II or higher within 30 days of randomisation. Participants and those delivering the intervention will not be blinded to treatment allocation; however, outcome assessors will be blinded when feasible. Participant recruitment started in January 2017 and is scheduled to last 3 years, within 50 hospitals worldwide. ETHICS/DISSEMINATION: The OPTIMISE II trial has been approved by the UK National Research Ethics Service and has been approved by responsible ethics committees in all participating countries. The findings will be disseminated through publication in a widely accessible peer-reviewed scientific journal. TRIAL REGISTRATION NUMBER: ISRCTN39653756.The OPTIMISE II trial is supported by Edwards Lifesciences (Irvine, CA) and the UK National Institute for Health Research through RMP’s NIHR Professorship

Technology acquisition and efficiency in Dubai hospitals

The paper studies the impact of the acquisition of relevant medical technology and information technology on the efficiency of hospital wards in three public hospitals in Dubai. Efficiency scores are obtained through bootstrapped data envelopment analysis, and are then regressed on variables assessing the extent of technology acquisition using truncated regression. Results show that both the acquisition of medical technology and of information technology have a positive impact on the ward efficiency, but that the strength of this relation is moderated by several variables related to organizational and managerial factors. In particular, results point out that the relationship between efficiency and technology is positively moderated by the ability of the head of ward to manage internal conflicts, by the managerial goals, and by the tenure of the head of ward. A negative moderating impact is exerted by perceived constraints to managerial actions, such as conflicting priorities with the hospital general management