Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline

Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is no exception. Despite the fact that Twitter data has been extensively used to understand socioeconomic and political phenomena and user behaviour, the implicit feedback provided by users on Tweets through their engagements on the Home Timeline has only been explored to a limited extent. At the same time, there is a lack of large-scale public social network datasets that would enable the scientific community to both benchmark and build more powerful and comprehensive models that tailor content to user interests. By releasing an original dataset of 160 million Tweets along with engagement information, Twitter aims to address exactly that. During this release, special attention is drawn on maintaining compliance with existing privacy laws. Apart from user privacy, this paper touches on the key challenges faced by researchers and professionals striving to predict user engagements. It further describes the key aspects of the RecSys 2020 Challenge that was organized by ACM RecSys in partnership with Twitter using this dataset.Comment: 16 pages, 2 table

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Effective scheduling of hospital personnel needs through forecasting daily emergency admissions

151 σ.Ο βασικός κοινωνικός στόχος του Παγκόσμιου Οργανισμού Υγείας και των κρατών-μελών του κατά τα τέλη του προηγούμενου αιώνα ήταν η εξασφάλιση για όλους τους ανθρώπους του κόσμου ενός επιπέδου υγείας μέχρι το 2000, που θα τους επέτρεπε να έχουν μια κοινωνικά και οικονομικά παραγωγική ζωή. Παρόλα αυτά, ακόμα και σήμερα, μία δεκαετία αργότερα, οι ανισότητες στον κλάδο της Υγείας είναι τόσο μεγάλες που, προκειμένου να επιτευχθεί αυτός ο στόχος, πρέπει να πραγματοποιηθεί μια σημαντική ανακατανομή των ανθρώπινων δυνάμεων, αλλά και να αλλάξει ριζικά ο τρόπος με τον οποίο οι ανθρώπινοι πόροι χρησιμοποιούνται για τη βελτίωση της υγείας. Το ανθρώπινο δυναμικό είναι ο πυλώνας του συστήματος υγείας κάθε χώρας, καθώς όλες οι μορφές της υγειονομικής περίθαλψης βασίζονται σε ένα καλά εκπαιδευμένο υγειονομικό προσωπικό. Σε πολλές χώρες έχει δοθεί ελάχιστη σημασία στο σχεδιασμό του ανθρώπινου δυναμικού, ενώ, αρκετές φορές, τα σχέδια που αναπτύχθηκαν κατέληξαν σε αποτυχία. Έτσι, λοιπόν, στη σημερινή εποχή, οι μάνατζερ του τομέα της υγείας έρχονται αντιμέτωποι με όλες τις σημαντικές προκλήσεις του 21ου αιώνα αλλά και με τους Στόχους Ανάπτυξης της Χιλιετίας. Πολλές από αυτές τις προκλήσεις προκύπτουν από τη δυσκολία εξασφάλισης μιας επαρκούς και κατάλληλης κατανομής του προσωπικού της υγείας, παράλληλα με τις αυξανόμενες οικονομικές πιέσεις που δέχεται ο δημόσιος τομέας για περιορισμό των δαπανών του. Η μέθοδος Δεικτών Φόρτου Εργασίας για τις Ανάγκες σε Προσωπικό (WISN) είναι μία αυστηρή μέθοδος που σαν στόχο έχει τον προσδιορισμό του πλήθους των εργαζομένων που απαιτούνται στις υγειονομικές εγκαταστάσεις. Οι δυνατότητες του κλάδου των προβλέψεων μπορούν να χρησιμοποιηθούν για την ολοκλήρωση και τη βελτίωση της ακρίβειας αυτής της μεθόδου, προκειμένου οι μάνατζερ ανθρώπινου δυναμικού να έχουν πλέον στα χέρια τους ένα ισχυρό και αποτελεσματικό εργαλείο για τη διαχείριση του προσωπικού που θα οδηγήσει στην καλύτερη προσφορά των υγειονομικών υπηρεσιών, την ισότητα στην πρόσβαση στις υπηρεσίες αυτές, ακόμα και στη μείωση των δαπανών για την Υγεία.The main social target of the World Health Organization and of its Member States at the end of the last century was to secure for all people of the world by the year 2000 a level of health that would allow them to lead a socially and economically productive life. However, such is the present inequality in the health status of the world’s people that, in order to reach this goal, there should be a substantial redistribution of health manpower and also a radical change of the way in which human resources are used to improve health. Health workforce is the cornerstone of every health system, since all forms of health care are based on a well-trained health personnel. In many countries, too little attention has been paid to health manpower planning, and sometimes, when plans have been developed, they proved to be inadequate and led to failure. As a result, current managers of the health sector are confronted with all these significant challenges of the 21st century as well as with the Millennium Development Goals. Many of these challenges arise from the difficulty of ensuring an adequate and appropriate distribution of health services, along with increasing financial pressures in the public sector to reduce its expenditure. The Workload Indicators of Staffing Need (WISN) method is a rigorous method which aims to determine the number of health workers required in health facilities. The potential of the field of forecasting can be employed in order to integrate this method and improve its accuracy, so as to create a powerful and effective tool for human resource managers. This tool can be used for personnel management in order to achieve better quality of health care services, equal access to these services and even reduced cost for the health sector.Σοφία-Ήρα Σ. Κτεν

Circulating osteopontin levels and outcomes in patients hospitalized for COVID-19

Background: Severe coronavirus disease 2019 (COVID-19) is the result of a hyper-inflammatory reaction to the severe acute respiratory syndrome coronavirus 2. The biomarkers of inflammation have been used to risk-stratify patients with COVID-19. Osteopontin (OPN) is an integrin-binding glyco-phosphoprotein involved in the modulation of leukocyte activation; its levels are associated with worse outcomes in patients with sepsis. Whether OPN levels predict outcomes in COVID-19 is unknown. Methods: We measured OPN levels in serum of 341 hospitalized COVID-19 patients collected within 48 h from admission. We characterized the determinants of OPN levels and examined their association with in-hospital outcomes; notably death, need for mechanical ventilation, and need for renal replacement therapy (RRT) and as a composite outcome. The risk discrimination ability of OPN was compared with other inflammatory biomarkers. Results: Patients with COVID-19 (mean age 60, 61.9% male, 27.0% blacks) had significantly higher levels of serum OPN compared to healthy volunteers (96.63 vs. 16.56 ng/mL, p < 0.001). Overall, 104 patients required mechanical ventilation, 35 needed dialysis, and 53 died during their hospitalization. In multivariable analyses, OPN levels ≥140.66 ng/mL (third tertile) were associated with a 3.5 × (95%CI 1.44–8.27) increase in the odds of death, and 4.9 × (95%CI 2.48–9.80) increase in the odds of requiring mechanical ventilation. There was no association between OPN and need for RRT. Finally, OPN levels in the upper tertile turned out as an independent prognostic factor of event-free survival with respect to the composite endpoint. Conclusion: Higher OPN levels are associated with increased odds of death and mechanical ventilation in patients with COVID-19, however, their utility in triage is questionable

Circulating Osteopontin Levels and Outcomes in Patients Hospitalized for COVID-19

Background: Severe coronavirus disease 2019 (COVID-19) is the result of a hyper-inflammatory reaction to the severe acute respiratory syndrome coronavirus 2. The biomarkers of inflammation have been used to risk-stratify patients with COVID-19. Osteopontin (OPN) is an integrin-binding glyco-phosphoprotein involved in the modulation of leukocyte activation; its levels are associated with worse outcomes in patients with sepsis. Whether OPN levels predict outcomes in COVID-19 is unknown. Methods: We measured OPN levels in serum of 341 hospitalized COVID-19 patients collected within 48 h from admission. We characterized the determinants of OPN levels and examined their association with in-hospital outcomes; notably death, need for mechanical ventilation, and need for renal replacement therapy (RRT) and as a composite outcome. The risk discrimination ability of OPN was compared with other inflammatory biomarkers. Results: Patients with COVID-19 (mean age 60, 61.9% male, 27.0% blacks) had significantly higher levels of serum OPN compared to healthy volunteers (96.63 vs. 16.56 ng/mL, p &lt; 0.001). Overall, 104 patients required mechanical ventilation, 35 needed dialysis, and 53 died during their hospitalization. In multivariable analyses, OPN levels &gt;= 140.66 ng/mL (third tertile) were associated with a 3.5 x (95%CI 1.44-8.27) increase in the odds of death, and 4.9 x (95%CI 2.48-9.80) increase in the odds of requiring mechanical ventilation. There was no association between OPN and need for RRT. Finally, OPN levels in the upper tertile turned out as an independent prognostic factor of event-free survival with respect to the composite endpoint. Conclusion: Higher OPN levels are associated with increased odds of death and mechanical ventilation in patients with COVID-19, however, their utility in triage is questionable

Early treatment of COVID-19 with anakinra guided by soluble urokinase plasminogen receptor plasma levels: a double-blind, randomized controlled phase 3 trial

Early increase of soluble urokinase plasminogen activator receptor (suPAR) serum levels is indicative of increased risk of progression of coronavirus disease 2019 (COVID-19) to respiratory failure. The SAVE-MORE double-blind, randomized controlled trial evaluated the efficacy and safety of anakinra, an IL-1 alpha/beta inhibitor, in 594 patients with COVID-19 at risk of progressing to respiratory failure as identified by plasma suPAR &gt;= 6 ng ml(-1), 85.9% (n = 510) of whom were receiving dexamethasone. At day 28, the adjusted proportional odds of having a worse clinical status (assessed by the 11-point World Health Organization Clinical Progression Scale (WHO-CPS)) with anakinra, as compared to placebo, was 0.36 (95% confidence interval 0.26-0.50). The median WHO-CPS decrease on day 28 from baseline in the placebo and anakinra groups was 3 and 4 points, respectively (odds ratio (OR) = 0.40, P &lt; 0.0001); the respective median decrease of Sequential Organ Failure Assessment (SOFA) score on day 7 from baseline was 0 and 1 points (OR = 0.63, P = 0.004). Twenty-eight-day mortality decreased (hazard ratio = 0.45, P = 0.045), and hospital stay was shorter.The SAVE-MORE phase 3 study demonstrates the efficacy of anakinra, an IL-1 alpha/beta inhibitor, in patients with COVID-19 and high serum levels of soluble plasminogen activator receptor