A first approach to earthquake damage estimation in Haiti: advices to minimize the seismic risk

This study is in the frame of the cooperative line that several Spanish Universities and other foreign partners started with the Haitian government in 2010. According to our studies (Benito et al. in An evaluation of seismic hazard in La Hispaniola, after the 2010 Haiti earthquake, 33rd General Assembly of the European Seismological Commission, Moscow, Russia, 2012) and recent scientific literature, the earthquake hazard in Haiti remains high (Calais et al. in Nat Geosci 3:794–799, 2010). In view of this, we wonder whether the country is currently ready to face another earthquake. In this sense, we estimated several damage scenarios in Port-au-Prince and Cap-Haitien associated to realistic possible major earthquakes. Our findings show that almost 50 % of the building stock of both cities would result uninhabitable due to structural damage. Around 80 % of the buildings in both cities have reinforced concrete structure with concrete block infill; however, the presence of masonry buildings becomes significant (between 25 and 45 % of the reinforced concrete buildings) in rural areas and informal settlements on the outskirts, where the estimated damage is higher. The influence of the soil effect on the damage spatial distribution is evident in both cities. We have found that the percentage of uninhabitable buildings in soft soil areas may be double the percentage obtained in nearby districts located in hard soil. These results reveal that a new seismic catastrophe of similar or even greater consequences than the 2010 Haiti earthquake might happen if the earthquake resilience is not improved in the country. Nowadays, the design of prevention actions and mitigation policies is the best instrument the society has to face seismic risk. In this sense, the results of this research might contribute to define measures oriented to earthquake risk reduction in Haiti, which should be a real priority for national and international institutions.

Aid decision algorithms to estimate the risk in congenital heart surgery

Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.