112.social: Design and Evaluation of a Mobile Crisis App for Bidirectional Communication between Emergency Services and Citizens

Emergencies threaten human lives and overall societal continuity, whether or not the crises and disasters are induced by nature, such as earthquakes, floods and hurricanes, or by human beings, such as accidents, terror attacks and uprisings. In such situations, not only do citizens demand information about the damage and safe behaviour, but emergency services also require high quality information to improve situational awareness. For this purpose, there are currently two kinds of apps available: General-purpose apps, such as Facebook Safety Check or Twitter Alerts, already integrate safety features. Specific crisis apps, such as KATWARN in Germany or FEMA in the US, provide information on how to behave before, during and after emergencies, and capabilities for reporting incidents or receiving disaster warnings. In this paper, we analyse authorities’ and citizens’ information demands and features of crisis apps. Moreover, we present the concept, implementation and evaluation of a crisis app for incident reporting and bidirectional communication between authorities and citizens. Using the app, citizens may (1) report incidents by providing a category, description, location and multimedia files and (2) receive broadcasts and responses from authorities. Finally, we outline features, requirements and contextual factors for incident reporting and bidirectional communication via mobile app

Semi-Automatic Alerts and Notifications for Emergency Services based on Cross-Platform Social Media Data - Evaluation of a Prototype

The convergence of social networking and mobile media technology is shifting the way how people communicate and gain or share information. People are using social media to a greater extent, also in emergency situations. During disasters throughout the world, such as the 2010 Haiti earthquake, the 2013 European floods, or the terror attacks 2015 in Paris and 2016 in Brussels, this has been illustrated again. Often information about disasters even finds its way faster to social media than it reaches regular news companies and emergency services. However, approaches for processing and analyzing the vast quantities of data produced have even more potential. Yet many emergency services still have not found a way to put this potential to an effective use. Within our project EmerGent we are developing a system to process and analyse information from social media particularly tailored for the specific needs of emergency services. The aim is to transform the high volume of noisy data into a low volume of rich content that is useful to emergency personnel. In the first part of this paper we present our approach from a user interface perspective. The second part deals with the evaluation of the approach and the derivation of future potentials of the approach

112.SOCIAL: Design and Evaluation of a Mobile Crisis App for Bidirectional Communication between Emergency Services and Citizen

Emergencies threaten human lives and overall societal continuity, whether or not the crises and disasters are induced by nature, such as earthquakes, floods and hurricanes, or by human beings, such as accidents, terror attacks and uprisings. In such situations, not only do citizens demand information about the damage and safe behaviour, but emergency services also require high quality information to improve situational awareness. For this purpose, there are currently two kinds of apps available: General-purpose apps, such as Facebook Safety Check or Twitter Alerts, already integrate safety features. Specific crisis apps, such as KATWARN in Germany or FEMA in the US, provide information on how to behave before, during and after emergencies, and capabilities for reporting incidents or receiving disaster warnings. In this paper, we analyse authorities’ and citizens’ information demands and features of crisis apps. Moreover, we present the concept, implementation and evaluation of a crisis app for incident reporting and bidirectional communication between authorities and citizens. Using the app, citizens may (1) report incidents by providing a category, description, location and multimedia files and (2) receive broadcasts and responses from authorities. Finally, we outline features, requirements and contextual factors for incident reporting and bidirectional communication via mobile app

Semi-Automatic Alerts and Notifications for Emergency Services based on Cross-Platform Social Media Data - Evaluation of a Prototype

The convergence of social networking and mobile media technology is shifting the way how people communicate and gain or share information. People are using social media to a greater extent, also in emergency situations. During disasters throughout the world, such as the 2010 Haiti earthquake, the 2013 European floods, or the terror attacks 2015 in Paris and 2016 in Brussels, this has been illustrated again. Often information about disasters even finds its way faster to social media than it reaches regular news companies and emergency services. However, approaches for processing and analyzing the vast quantities of data produced have even more potential. Yet many emergency services still have not found a way to put this potential to an effective use. Within our project EmerGent we are developing a system to process and analyse information from social media particularly tailored for the specific needs of emergency services. The aim is to transform the high volume of noisy data into a low volume of rich content that is useful to emergency personnel. In the first part of this paper we present our approach from a user interface perspective. The second part deals with the evaluation of the approach and the derivation of future potentials of the approach

A six-year teaching life supportive first aid program to eventually generate peer trainer pupils: a prospective case control study

Abstract Background Out of hospital cardiac arrest is a life-threatening condition. To improve the chances of survival, lay-person cardio-pulmonary-resuscitation (CPR) is a crucial factor. Many bystanders fail to react appropriately, even if life supporting first aid (LSFA) programs and campaigns including CPR tried to increase the handling of basic cardiac life support. To achieve an enhanced learning of CPR a pupil’s grade after grade teaching program was established in a school with medical students. Methods The learning of CPR was investigated in a prospective, case-controlled study at an international school. Pupils (12 ± 3 years old) joining our LSFA courses (n = 538, female: 243, attendance for evaluation: 476) were compared to a control group (n = 129, female: 52, attendance for evaluation: 102). Surveys and quality of CPR (QCPR%) through a computer linked “Resusci Anne” dummy were compared with Chi-squared tests, t-tests pair wisely, and by one-way ANOVA. Results Knowledge and skills on the “Resusci Anne” were significantly better in trained grade 9 pupils compared to the control group (QCPR, 59 vs. 25%). The number of LSFA courses each grade 9 student had, correlated with improved practical performance (r2 = 0.21, p < 0.001). The willingness to deliver CPR to strangers increased with improved practical performance. Attitudes towards performing CPR were high in all participating grades. Conclusion Repetitive teaching LSFA to grade 5–9 pupil’s grade after grade by medical students has been successfully established. Pupils who finish the program will eventually be able to teach LSFA to younger students. This is furthermore a good way of sharing a “learning by teaching” role and it enables to have more pupils as trainers who can provide instruction to a larger number of pupils with the purpose of having a better-trained population in LSFA

The Gene Expression Classifier ALLCatchR Identifies B-cell Precursor ALL Subtypes and Underlying Developmental Trajectories Across Age

Current classifications (World Health Organization-HAEM5/ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by transcriptome sequencing (RNA-Seq) is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning-based classifier using RNA-Seq gene expression data to allocate BCP-ALL samples to all 21 gene expression-defined molecular subtypes. Trained on n = 1869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n = 1018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). High-confidence predictions were achieved in 83.7% of samples with 98.9% accuracy. Only 1.2% of samples remained unclassified. ALLCatchR outperformed existing tools and identified novel driver candidates in previously unassigned samples. Additional modules provided predictions of samples blast counts, patient’s sex, and immunophenotype, allowing the imputation in cases where these information are missing. We established a novel RNA-Seq reference of human B-lymphopoiesis using 7 FACS-sorted progenitor stages from healthy bone marrow donors. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory. This identified shared proximity patterns of BCP-ALL subtypes to normal lymphopoiesis stages, extending immunophenotypic classifications with a novel framework for developmental comparisons of BCP-ALL. ALLCatchR enables RNA-Seq routine application for BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocation and novel insights into underlying developmental trajectories