The COVID-19 pandemic has been spreading worldwide since December 2019, presenting an urgent threat to global health. Due to the limited understanding of disease progression and of the risk factors for the disease, it is a clinical challenge to predict which hospitalized patients will deteriorate. Moreover, several studies suggested that taking early measures for treating patients at risk of deterioration could prevent or lessen condition worsening and the need for mechanical ventilation. We developed a predictive model for early identification of patients at risk for clinical deterioration by retrospective analysis of electronic health records of COVID-19 inpatients at the two largest medical centers in Israel. Our model employs machine learning methods and uses routine clinical features such as vital signs, lab measurements, demographics, and background disease. Deterioration was defined as a high NEWS2 score adjusted to COVID-19. In the prediction of deterioration within the next 7–30 h, the model achieved an area under the ROC curve of 0.84 and an area under the precision-recall curve of 0.74. In external validation on data from a different hospital, it achieved values of 0.76 and 0.7, respectively.
This study evaluated physical activity Web sites to determine quality, accuracy, and consistency with principles of the extended parallel process model (EPPM). Three keyword searches were conducted using 4 search engines to find a sample of N = 41 Web sites. Three raters evaluated the Web sites using the JAMA benchmarks to assess quality and American College of Sports Medicine and Centers for Disease Control and Prevention recommendations for physical activity to determine accuracy, as well as checking for inclusion of EPPM variables. Data were analyzed using descriptive statistics and analysis of variance with least squares means. Only 22% of the sites were high quality, none were highly accurate, and most were consistent with the EPPM. Quality ratings were weakly associated with accuracy. Educational and .net sites were rated significantly higher in quality and accuracy, and government sites were most consistent with the EPPM. Quality Web sites were more often found by using Yahoo and Google. "Exercise" yielded more accurate results, whereas "physical activity" and "fitness" produced more Web sites consistent with the EPPM. It is encouraging that most sites incorporated EPPM concepts; however, quality and accuracy were poor, leaving physical activity information seekers at risk for disease and injury.
A: Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural networks, can be used for inverse problems such as plasma tomography. In essence, plasma tomography consists in reconstructing the 2D plasma profile on a poloidal cross-section of a fusion device, based on line-integrated measurements from multiple radiation detectors. Since the reconstruction process is computationally intensive, a deconvolutional neural network trained to produce the same results will yield a significant computational speedup, at the expense of a small error which can be assessed using different metrics. In this work, we discuss the design principles behind such networks, including the use of multiple layers, how they can be stacked, and how their dimensions can be tuned according to the number of detectors and the desired tomographic resolution for a given fusion device. We describe the application of such networks at JET and COMPASS, where at JET we use the bolometer system, and at COMPASS we use the soft X-ray diagnostic based on photodiode arrays. K : Computerized Tomography (CT) and Computed Radiography (CR); Plasma diagnostics -interferometry, spectroscopy and imaging 1Corresponding author. 2See the author list of Overview of the JET preparation for Deuterium-Tritium Operation by E. Joffrin et al. in Nucl.
Background To assess the utility of C-reactive protein (CRP) velocity to discriminate between patients with acute viral and bacterial infections who presented with relatively low CRP concentrations and were suspected of having a bacterial infection. Methods We analyzed a retrospective cohort of patients with acute infections who presented to the emergency department (ED) with a relatively low first CRP measurement (CRP1) ≤ 31.9 mg/L and received antibiotics shortly after. We then calculated C-reactive protein velocity (CRPv), milligram per liter per hour, for each patient based on CRP1 and the second CRP value (CRP2) measured within the first 24 h since admission. Finally, we compared CRPv between patients with bacterial and viral infections. Results We have presently analyzed 74 patients with acute bacterial infections and 62 patients with acute viral infections at the mean age of 80 and 66 years respectively, 68 male and 68 female. CRP1 did not differ between both groups of patients (16.2 ± 8.6 and 14.8 ± 8.5 for patients with viral and bacterial infections respectively, p value = 0.336). However, the CRP2 was significantly different between the groups (30.2 ± 21.9 and 75.6 ± 51.3 for patients with viral and bacterial infections respectively, p-value < 0.001) and especially the CRPv was much higher in patients with acute bacterial infections compared to patients with acute viral infections (0.9 ± 1.2 and 4.4 ± 2.7 respectively, p-value < 0.001). Conclusion CRPv and CRP2 are useful biomarkers that can discriminate significantly between patients who present with acute bacterial and viral infections, and relatively low CRP concentration upon admission who were suspected of having a bacterial infection.
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