Learning Early Detection of Emergencies from Word Usage Patterns on Social Media

In the early stages of an emergency, information extracted from social media can support crisis response with evidence-based content. In order to capture this evidence, the events of interest must be first promptly detected. An automated detection system is able to activate other tasks, such as preemptive data processing for extracting eventrelated information. In this paper, we extend the human-in-the-loop approach in our previous work, TriggerCit, with a machine-learning-based event detection system trained on word count time series and coupled with an automated lexicon building algorithm.We design this framework in a language-agnostic fashion. In this way, the system can be deployed to any language without substantial effort. We evaluate the capacity of the proposed work against authoritative flood data for Nepal recorded over two years

TriggerCit: Early Flood Alerting using Twitter and Geolocation - A Comparison with Alternative Sources

Rapid impact assessment in the immediate aftermath of a natural disaster is essential to provide adequate information to international organisations, local authorities, and first responders. Social media can support emergency response with evidence-based content posted by citizens and organisations during ongoing events. In the paper, we propose TriggerCit: an early flood alerting tool with a multilanguage approach focused on timeliness and geolocation. The paper focuses on assessing the reliability of the approach as a triggering system, comparing it with alternative sources for alerts, and evaluating the quality and amount of complementary information gathered. Geolocated visual evidence extracted from Twitter by TriggerCit was analysed in two case studies on floods in Thailand and Nepal in 2021.Comment: 12 pages Keywords Social Media, Disaster management, Early Alertin

The usual Interstitial pneumonia pattern in autoimmune rheumatic diseases

: Usual Interstitial Pneumonia (UIP) is characterized by progression of lung parenchyma that may be observed in various autoimmune rheumatic diseases (ARDs), including rheumatoid arthritis and connective tissue diseases. From a diagnostic point of view, a UIP pattern related to ARDs may display imaging and pathological features able to distinguish it from that related to IPF, such as the "straight-edge" sign at HRCT and lymphoplasmacytic infiltrates at histologic specimens. Multidisciplinary approach (MDD), involving at least pulmonologist, rheumatologist and radiologist, is fundamental in the differential diagnosis process, but MDD is also required in the evaluation of severity, progression and response to treatment, that is based on the combination of changes in symptoms, pulmonary function trends, and, in selected patients, serial CT evaluation. Differently from IPF, in patients with ARDs both functional evaluation and patient-reported outcomes may be affected by systemic involvement and comorbidities, including musculoskeletal manifestations of disease. Finally, in regards to pharmacological treatment, immunosuppressants have been considered the cornerstone of therapy, despite the lack of solid evidence in most cases; recently, antifibrotic drugs were also proposed for the treatment of progressive fibrosing ILDs other than IPF. In ARD-ILD, the therapeutic choice should balance the need for the control of systemic and lung involvements with the risk of adverse events from multi-morbidities and -therapies. Purpose of this review is to summarize the definition, the radiological and morphological features of the UIP pattern in ARDs, together with risk factors, diagnostic criteria, prognostic evaluation, monitoring and management approaches of the UIP-ARDs

A Citizen Science Approach for Analyzing Social Media With Crowdsourcing

Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among the millions of posts being added every day can be difficult, and in current approaches developing an automatic data analysis project requires time and technical skills. This work presents a new approach for the analysis of social media posts, based on configurable automatic classification combined with Citizen Science methodologies. The process is facilitated by a set of flexible, automatic and open-source data processing tools called the Citizen Science Solution Kit. The kit provides a comprehensive set of tools that can be used and personalized in different situations, particularly during natural emergencies, starting from images and text contained in the posts. The tools can be employed by citizen scientists for filtering, classifying, and geolocating the content with a human-in-the-loop approach to support the data analyst, including feedback and suggestions on how to configure the automated tools, and techniques to gather inputs from citizens. Using flooding scenario as a guiding example, this paper illustrates the structure and functioning of the different tools proposed to support citizens scientists in their projects, and a methodological approach to their use. The process is then validated by discussing three case studies based on the Albania earthquake of 2019, the Covid-19 pandemic, and the Thailand floods of 2021. The results suggest that a flexible approach to tools composition and configuration can support a timely setup of an analysis project by citizen scientists, especially in case of emergencies in unexpected locations.ISSN:2169-353

Risk factors for infections due to carbapenem-resistant Klebsiella pneumoniae after open heart surgery

OBJECTIVES Patients undergoing major surgery are at increased risk of developing infections due to resistant organisms, including carbapenem-resistant Klebsiella pneumoniae (CR-Kp). In this study, we assessed risk factors for CR-Kp infections after open heart surgery in a teaching hospital in northern Italy. METHODS A retrospective study was conducted from January to December 2014. The primary outcome measure was postoperative CR-Kp infection, defined as a time-to-event end-point. The effect of potentially related variables was assessed by univariable and multivariable analyses. Secondary end-points were in-hospital mortality and 180-day postoperative mortality. RESULTS Among 553 patients undergoing open heart surgery, 32 developed CR-Kp infections (6%). In the final multivariable model, CR-Kp colonization [hazard ratio (HR) 227.45, 95% confidence intervals (CI) 67.13-1225.20, P < 0.001], cardiopulmonary bypass time in minutes (HR 1.01, 95% CI 1.01-1.02, P < 0.001), chronic obstructive pulmonary disease (HR 3.99, 95% CI 1.61-9.45, P = 0.004), SOFA score (HR 1.29, 95% CI 1.08-1.53, P = 0.007), preoperative mechanical ventilation (HR 8.10, 95% CI 1.31-48.57, P = 0.026), prolonged mechanical ventilation (HR 2.48, 95% CI 1.08-6.15, P = 0.032) and female sex (HR 2.08, 95% CI 1.00-4.36, P = 0.049) were associated with the development of CR-Kp infection. Increased in-hospital mortality and 180-day mortality were observed in patients who developed CR-Kp infections in comparison with those who did not. CONCLUSIONS In our cohort, CR-Kp colonization was an important predictor of CR-Kp infection after open heart surgery. CR-Kp infection after surgery significantly affected survival. Preventing colonization is conceivably the most effective current strategy to reduce the impact of CR-Kp