10 research outputs found

    Resource Saving via Ensemble Techniques for Quantum Neural Networks

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    Quantum neural networks hold significant promise for numerous applications, particularly as they can be executed on the current generation of quantum hardware. However, due to limited qubits or hardware noise, conducting large-scale experiments often requires significant resources. Moreover, the output of the model is susceptible to corruption by quantum hardware noise. To address this issue, we propose the use of ensemble techniques, which involve constructing a single machine learning model based on multiple instances of quantum neural networks. In particular, we implement bagging and AdaBoost techniques, with different data loading configurations, and evaluate their performance on both synthetic and real-world classification and regression tasks. To assess the potential performance improvement under different environments, we conduct experiments on both simulated, noiseless software and IBM superconducting-based QPUs, suggesting these techniques can mitigate the quantum hardware noise. Additionally, we quantify the amount of resources saved using these ensemble techniques. Our findings indicate that these methods enable the construction of large, powerful models even on relatively small quantum devices.Comment: Extended paper of the work presented at QTML 2022. Close to published versio

    Fatality rate and predictors of mortality in an Italian cohort of hospitalized COVID-19 patients

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    Clinical features and natural history of coronavirus disease 2019 (COVID-19) differ widely among different countries and during different phases of the pandemia. Here, we aimed to evaluate the case fatality rate (CFR) and to identify predictors of mortality in a cohort of COVID-19 patients admitted to three hospitals of Northern Italy between March 1 and April 28, 2020. All these patients had a confirmed diagnosis of SARS-CoV-2 infection by molecular methods. During the study period 504/1697 patients died; thus, overall CFR was 29.7%. We looked for predictors of mortality in a subgroup of 486 patients (239 males, 59%; median age 71 years) for whom sufficient clinical data were available at data cut-off. Among the demographic and clinical variables considered, age, a diagnosis of cancer, obesity and current smoking independently predicted mortality. When laboratory data were added to the model in a further subgroup of patients, age, the diagnosis of cancer, and the baseline PaO2/FiO2 ratio were identified as independent predictors of mortality. In conclusion, the CFR of hospitalized patients in Northern Italy during the ascending phase of the COVID-19 pandemic approached 30%. The identification of mortality predictors might contribute to better stratification of individual patient risk

    How different countries respond to adverse events whilst patients’ rights are protected

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    Patient safety is high on the policy agenda internationally. Learning from safety incidents is a core component in achieving the important goal of increasing patient safety. This study explores the legal frameworks in the countries to promote reporting, disclosure, and supporting healthcare professionals (HCPs) involved in safety incidents. A cross-sectional online survey was conducted to ascertain an overview of the legal frameworks at national level, as well as relevant policies. ERNST (The European Researchers' Network Working on Second Victims) group peer-reviewed data collected from countries was performed to validate information. Information from 27 countries was collected and analyzed, giving a response rate of 60%. A reporting system for patient safety incidents was in place in 85.2% (N = 23) of countries surveyed, though few (37%, N = 10) were focused on systems-learning. In about half of the countries (48.1%, N = 13) open disclosure depends on the initiative of HCPs. The tort liability system was common in most countries. No-fault compensation schemes and alternative forms of redress were less common. Support for HCPs involved in patient safety incidents was extremely limited, with just 11.1% (N = 3) of participating countries reporting that supports were available in all healthcare institutions. Despite progress in the patient safety movement worldwide, the findings suggest that there are considerable differences in the approach to the reporting and disclosure of patient safety incidents. Additionally, models of compensation vary limiting patients' access to redress. Finally, the results highlight the need for comprehensive support for HCPs involved in safety incidents

    the ERNST study

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    The COVID-19 pandemic led to the implementation of interventions to provide emotional and psychological support to healthcare workers in many countries. This ecological study aims to describe the strategies implemented in different countries to support healthcare professionals during the outbreak. Data were collected through an online survey about the measures to address the impact of the pandemic on the mental health of healthcare workers. Healthcare professionals, researchers, and academics were invited to respond to the survey. Fifty-six professionals from 35 countries contributed data to this study. Ten countries (28.6%) reported that they did not launch any national interventions. Both developed and developing countries launched similar initiatives. There was no relationship between the existence of any type of initiative in a country with the incidence, lethality, and mortality rates of the country due to COVID-19, and per capita income in 2020. The 24 h hotline for psychological support was the most frequent intervention. Tools for self-rescue by using apps or websites were extensively used, too. Other common interventions were the development of action protocols, availability of regular and updated information, implantation of distance learning systems, early detection of infection programs for professionals, economic reinforcements, hiring of staff reinforcement, and modification of leave and vacation dates.publishersversionpublishe

    Simple Parameters from Complete Blood Count Predict In-Hospital Mortality in COVID-19

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    Introduction. The clinical course of Coronavirus Disease 2019 (COVID-19) is highly heterogenous, ranging from asymptomatic to fatal forms. The identification of clinical and laboratory predictors of poor prognosis may assist clinicians in monitoring strategies and therapeutic decisions. Materials and Methods. In this study, we retrospectively assessed the prognostic value of a simple tool, the complete blood count, on a cohort of 664 patients (F 260; 39%, median age 70 (56-81) years) hospitalized for COVID-19 in Northern Italy. We collected demographic data along with complete blood cell count; moreover, the outcome of the hospital in-stay was recorded. Results. At data cut-off, 221/664 patients (33.3%) had died and 453/664 (66.7%) had been discharged. Red cell distribution width (RDW) (χ2 10.4; p4.68 was characterized by an odds ratio for in-hospital mortality OR=3.40 (2.40-4.82), while the OR for a RDW>13.7% was 4.09 (2.87-5.83); a platelet count>166,000/μL was, conversely, protective (OR: 0.45 (0.32-0.63)). Conclusion. Our findings arise the opportunity of stratifying COVID-19 severity according to simple lab parameters, which may drive clinical decisions about monitoring and treatment

    Search for a heavy composite Majorana neutrino in events with dilepton signatures from proton-proton collisions at <math altimg="si1.svg"><msqrt><mrow><mi>s</mi></mrow></msqrt><mo linebreak="goodbreak" linebreakstyle="after">=</mo><mn>13</mn><mtext> TeV</mtext></math>

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    International audienceResults are presented of a search for a heavy Majorana neutrino Image 1 decaying into two same-flavor leptons ℓ (electrons or muons) and a quark-pair jet. A model is considered in which the Image 1 is an excited neutrino in a compositeness scenario. The analysis is performed using a sample of proton-proton collisions at s=13TeV recorded by the CMS experiment at the CERN LHC, corresponding to an integrated luminosity of 138fb−1. The data are found to be in agreement with the standard model prediction. For the process in which the Image 1 is produced in association with a lepton, followed by the decay of the Image 1 to a same-flavor lepton and a quark pair, an upper limit at 95% confidence level on the product of the cross section and branching fraction is obtained as a function of the Image 1 mass Image 2 and the compositeness scale Λ. For this model the data exclude the existence of Image 3 (Image 4) for Image 2 below 6.0 (6.1) TeV, at the limit where Image 2 is equal to Λ. For Image 5, values of Λ less than 20 (23) TeV are excluded. These results represent a considerable improvement in sensitivity, covering a larger parameter space than previous searches in Image 6 collisions at 13 TeV
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