Computational medicine, present and the future: obstetrics and gynecology perspective.

Medicine is, in its essence, decision making under uncertainty; the decisions are made about tests to be performed and treatments to be administered. Traditionally, the uncertainty in decision making was handled using expertise collected by individual providers and, more recently, systematic appraisal of research in the form of evidence-based medicine. The traditional approach has been used successfully in medicine for a very long time. However, it has substantial limitations because of the complexity of the system of the human body and healthcare. The complex systems are a network of highly coupled components intensely interacting with each other. These interactions give those systems redundancy and thus robustness to failure and, at the same time, equifinality, that is, many different causative pathways leading to the same outcome. The equifinality of the complex systems of the human body and healthcare system demand the individualization of medical care, medicine, and medical decision making. Computational models excel in modeling complex systems and, consequently, enabling individualization of medical decision making and medicine. Computational models are theory- or knowledge-based models, data-driven models, or models that combine both approaches. Data are essential, although to a different degree, for computational models to successfully represent complex systems. The individualized decision making, made possible by the computational modeling of complex systems, has the potential to revolutionize the entire spectrum of medicine from individual patient care to policymaking. This approach allows applying tests and treatments to individuals who receive a net benefit from them, for whom benefits outweigh the risk, rather than treating all individuals in a population because, on average, the population benefits. Thus, the computational modeling-enabled individualization of medical decision making has the potential to both improve health outcomes and decrease the costs of healthcare

A Nonintrusive, Adaptable and User-Friendly In Situ Visualization Framework

Reducing the amount of data stored by simulations will be of utmost importance for the next generation of large-scale computing. Accordingly, there is active research to shift analysis and visualization tasks to run in situ, i.e. closer to the simulation via the sharing of some resources. This is beneficial as it can avoid the necessity of storing large amounts of data for post-processing. In this paper, we focus on the specific case of in situ visualization where analysis codes are collocated with the simulation's code and run on the same resources. It is important for an in situ technique to require minimum modifications to existing codes, be adaptable and have a low impact on both run times and resource usage. We accomplish this through the Damaris/Viz framework, which provides in situ visualization support to the Damaris I/O middleware. The use of Damaris as a bridge to existing visualization packages allows us to (1) reduce code moditications to a minimum for existing simulations, (2) gather capabilities of several visualization tools to offer a unified data management interface, (3) use dedicated cores to hide the run time impact of in situ visualization and (4) efficiently use memory through a shared-memory-based communication model. Experiments are conducted on Blue Waters and Grid5000 to visualize the CM1 atmospheric simulation and the Nek5000 CFD solver

Damaris/Viz: a Nonintrusive, Adaptable and User-Friendly In Situ Visualization Framework

International audienceReducing the amount of data stored by simulations will be of utmost importance for the next generation of large-scale computing. Accordingly, there is active research to shift analysis and visualization tasks to run in situ, that is, closer to the simulation via the sharing of some resources. This is beneficial as it can avoid the necessity of storing large amounts of data for post-processing. In this paper, we focus on the specific case of in situ visualization where analysis codes are collocated with the simulation's code and run on the same resources. It is important for an in situ technique to require minimum modifications to existing codes, be adaptable, and have a low impact on both run times and resource usage. We accomplish this through the Damaris/Viz framework, which provides in situ visualization support to the Damaris I/O middleware. The use of Damaris as a bridge to existing visualization packages allows us to (1) reduce code moditication to a minimum for existing simulations, (2) gather capabilities of several visualization tools to offer a unified data management interface, (3) use dedicated cores to hide the run time impact of in situ visualization and (4) efficiently use memory through a shared-memory-based communication model. Experiments are conducted on Blue Waters and Grid'5000 to visualize the CM1 atmospheric simulation and the Nek5000 CFD solver

Description of Emergency Medical Services, treatment of cardiac arrest patients and cardiac arrest registries in Europe

Background: Variation in the incidence, survival rate and factors associated with survival after cardiac arrest in Europe is reported. Some studies have tried to fill the knowledge gap regarding the epidemiology of out-of-hospital cardiac arrest in Europe but were unable to identify reasons for the reported differences. Therefore, the purpose of this study was to describe European Emergency Medical Systems, particularly from the perspective of country and ambulance service characteristics, cardiac arrest identification, dispatch, treatment, and monitoring. Methods: An online questionnaire with 51 questions about ambulance and dispatch characteristics, on-scene management of cardiac arrest and the availability and dataset in cardiac arrest registries, was sent to all national coordinators who participated in the European Registry of Cardiac Arrest studies. In addition, individual invitations were sent to the remaining European countries. Results: Participants from 28 European countries responded to the questionnaire. Results were combined with official information on population density. Overall, the number of Emergency Medical Service missions, level of training of personnel, availability of Helicopter Emergency Medical Services and the involvement of first responders varied across and within countries. There were similarities in team training, availability of key resuscitation equipment and permission for ongoing performance of cardiopulmonary resuscitation during transported. The quality of reporting to cardiac arrest registries varied, as well as the data availability in the registries. Conclusions: Throughout Europe there are important differences in Emergency Medical Service systems and the response to out-of-hospital cardiac arrest. Explaining these differences is complicated due to significant variation in how variables are reported to and used in registries