Cerca de trajectòries de pacients a través de les etapes d'una malaltia a partir d'històries digitals

Aquest projecte sorgeix d’una col·laboració entre l’empresa Serveis Mèdics i el grup de recerca LARCA de la UPC. Vol aportar una manera diferent d’analitzar les dades de que disposen els professionals de la medicina. S’han treballat diferents tècniques de mineria de dades per a aquest propòsit i les seleccionades finalment han estat encapsulades dins d’una aplicació. Es vol que aquesta aplicació sigui utilitzada pels metges i gestors de recursos amb les dades de diagnòstics de malalties de pacients, permetent una forma ràpida d’analitzar aquestes dades que d’altra manera no s’hauria arribat a aconseguir. L’aplicació permet guardar documents amb les dades dels pacients. Aquestes dades s’utilitzen a l’aplicació per a trobar patrons freqüents en les malalties, generar models predictius, grafs i estadística descriptiva. Finalment quan s’han obtingut els resultats, aquests es poden exportar. Per acabar es fa un anàlisi d’unes dades proporcionades per Serveis Mèdics per a provar l’aplicació. Es fan servir totes les funcionalitats i s’extreuen conclusions.This project starts from collaboration between the company Serveis Mèdics and LARCA research group of the UPC. It wants to bring a different way of analyzing the data available to medical professionals. For this purpose a search between different data mining techniques has been done and finally the selected ones have been encapsulated in an application. With the data of patient’s diseases it is wanted to use this application by physicians and resource managers, allowing a quick way to analyze these data that otherwise could not be achieved. The application allows saving documents with patient data. These data is used in the application to find frequent patterns in diseases, generate predictive models, graphs and descriptive statistics. Finally when the results have been obtained, these can be exported. Finally, an analysis of data provided by Serveis Mèdics to test the application is made. All features of the application are used and conclusions are drawn.Este proyecto surge de una colaboración entre la empresa Serveis Mèdics y el grupo de investigación LARCA de la UPC. Quiere aportar una manera diferente de analizar los datos de que disponen los profesionales de la medicina. Se han trabajado diferentes técnicas de minería de datos para este propósito y las seleccionadas finalmente se han encapsulado en una aplicación. Se quiere que esta aplicación sea utilizada por médicos y gestores de recursos con los datos de diagnósticos de enfermedades de pacientes, permitiendo una forma rápida de analizar estos datos que de otra forma no se podría conseguir. La aplicación permite guardar documentos con los datos de los pacientes. Estos datos se utilizan en la aplicación para encontrar patrones frecuentes en las enfermedades, generar modelos predictivos, grafos y estadística descriptiva. Finalmente cuando se han obtenido los resultados, estos se pueden exportar. Finalmente se hace un análisis de unos datos proporcionados por Serveis Mèdics para probar la aplicación. Se usan todas las funcionalidades y se extraen conclusiones

Transparent Orchestration of Task-based Parallel Applications in Containers Platforms

This paper presents a framework to easily build and execute parallel applications in container-based distributed computing platforms in a user-transparent way. The proposed framework is a combination of the COMP Superscalar (COMPSs) programming model and runtime, which provides a straightforward way to develop task-based parallel applications from sequential codes, and containers management platforms that ease the deployment of applications in computing environments (as Docker, Mesos or Singularity). This framework provides scientists and developers with an easy way to implement parallel distributed applications and deploy them in a one-click fashion. We have built a prototype which integrates COMPSs with different containers engines in different scenarios: i) a Docker cluster, ii) a Mesos cluster, and iii) Singularity in an HPC cluster. We have evaluated the overhead in the building phase, deployment and execution of two benchmark applications compared to a Cloud testbed based on KVM and OpenStack and to the usage of bare metal nodes. We have observed an important gain in comparison to cloud environments during the building and deployment phases. This enables better adaptation of resources with respect to the computational load. In contrast, we detected an extra overhead during the execution, which is mainly due to the multi-host Docker networking.This work is partly supported by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316 project, by the Generalitat de Catalunya under contracts 2014-SGR-1051 and 2014-SGR-1272, and by the European Union through the Horizon 2020 research and innovation program under grant 690116 (EUBra-BIGSEA Project). Results presented in this paper were obtained using the Chameleon testbed supported by the National Science Foundation.Peer ReviewedPostprint (author's final draft

EPIdemiology of Surgery-Associated Acute Kidney Injury (EPIS-AKI) : Study protocol for a multicentre, observational trial

More than 300 million surgical procedures are performed each year. Acute kidney injury (AKI) is a common complication after major surgery and is associated with adverse short-term and long-term outcomes. However, there is a large variation in the incidence of reported AKI rates. The establishment of an accurate epidemiology of surgery-associated AKI is important for healthcare policy, quality initiatives, clinical trials, as well as for improving guidelines. The objective of the Epidemiology of Surgery-associated Acute Kidney Injury (EPIS-AKI) trial is to prospectively evaluate the epidemiology of AKI after major surgery using the latest Kidney Disease: Improving Global Outcomes (KDIGO) consensus definition of AKI. EPIS-AKI is an international prospective, observational, multicentre cohort study including 10 000 patients undergoing major surgery who are subsequently admitted to the ICU or a similar high dependency unit. The primary endpoint is the incidence of AKI within 72 hours after surgery according to the KDIGO criteria. Secondary endpoints include use of renal replacement therapy (RRT), mortality during ICU and hospital stay, length of ICU and hospital stay and major adverse kidney events (combined endpoint consisting of persistent renal dysfunction, RRT and mortality) at day 90. Further, we will evaluate preoperative and intraoperative risk factors affecting the incidence of postoperative AKI. In an add-on analysis, we will assess urinary biomarkers for early detection of AKI. EPIS-AKI has been approved by the leading Ethics Committee of the Medical Council North Rhine-Westphalia, of the Westphalian Wilhelms-University Münster and the corresponding Ethics Committee at each participating site. Results will be disseminated widely and published in peer-reviewed journals, presented at conferences and used to design further AKI-related trials. Trial registration number NCT04165369

Cerca de trajectòries de pacients a través de les etapes d'una malaltia a partir d'històries digitals

Aquest projecte sorgeix d’una col·laboració entre l’empresa Serveis Mèdics i el grup de recerca LARCA de la UPC. Vol aportar una manera diferent d’analitzar les dades de que disposen els professionals de la medicina. S’han treballat diferents tècniques de mineria de dades per a aquest propòsit i les seleccionades finalment han estat encapsulades dins d’una aplicació. Es vol que aquesta aplicació sigui utilitzada pels metges i gestors de recursos amb les dades de diagnòstics de malalties de pacients, permetent una forma ràpida d’analitzar aquestes dades que d’altra manera no s’hauria arribat a aconseguir. L’aplicació permet guardar documents amb les dades dels pacients. Aquestes dades s’utilitzen a l’aplicació per a trobar patrons freqüents en les malalties, generar models predictius, grafs i estadística descriptiva. Finalment quan s’han obtingut els resultats, aquests es poden exportar. Per acabar es fa un anàlisi d’unes dades proporcionades per Serveis Mèdics per a provar l’aplicació. Es fan servir totes les funcionalitats i s’extreuen conclusions.This project starts from collaboration between the company Serveis Mèdics and LARCA research group of the UPC. It wants to bring a different way of analyzing the data available to medical professionals. For this purpose a search between different data mining techniques has been done and finally the selected ones have been encapsulated in an application. With the data of patient’s diseases it is wanted to use this application by physicians and resource managers, allowing a quick way to analyze these data that otherwise could not be achieved. The application allows saving documents with patient data. These data is used in the application to find frequent patterns in diseases, generate predictive models, graphs and descriptive statistics. Finally when the results have been obtained, these can be exported. Finally, an analysis of data provided by Serveis Mèdics to test the application is made. All features of the application are used and conclusions are drawn.Este proyecto surge de una colaboración entre la empresa Serveis Mèdics y el grupo de investigación LARCA de la UPC. Quiere aportar una manera diferente de analizar los datos de que disponen los profesionales de la medicina. Se han trabajado diferentes técnicas de minería de datos para este propósito y las seleccionadas finalmente se han encapsulado en una aplicación. Se quiere que esta aplicación sea utilizada por médicos y gestores de recursos con los datos de diagnósticos de enfermedades de pacientes, permitiendo una forma rápida de analizar estos datos que de otra forma no se podría conseguir. La aplicación permite guardar documentos con los datos de los pacientes. Estos datos se utilizan en la aplicación para encontrar patrones frecuentes en las enfermedades, generar modelos predictivos, grafos y estadística descriptiva. Finalmente cuando se han obtenido los resultados, estos se pueden exportar. Finalmente se hace un análisis de unos datos proporcionados por Serveis Mèdics para probar la aplicación. Se usan todas las funcionalidades y se extraen conclusiones

Transparent Orchestration of Task-based Parallel Applications in Containers Platforms

This paper presents a framework to easily build and execute parallel applications in container-based distributed computing platforms in a user-transparent way. The proposed framework is a combination of the COMP Superscalar (COMPSs) programming model and runtime, which provides a straightforward way to develop task-based parallel applications from sequential codes, and containers management platforms that ease the deployment of applications in computing environments (as Docker, Mesos or Singularity). This framework provides scientists and developers with an easy way to implement parallel distributed applications and deploy them in a one-click fashion. We have built a prototype which integrates COMPSs with different containers engines in different scenarios: i) a Docker cluster, ii) a Mesos cluster, and iii) Singularity in an HPC cluster. We have evaluated the overhead in the building phase, deployment and execution of two benchmark applications compared to a Cloud testbed based on KVM and OpenStack and to the usage of bare metal nodes. We have observed an important gain in comparison to cloud environments during the building and deployment phases. This enables better adaptation of resources with respect to the computational load. In contrast, we detected an extra overhead during the execution, which is mainly due to the multi-host Docker networking.This work is partly supported by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316 project, by the Generalitat de Catalunya under contracts 2014-SGR-1051 and 2014-SGR-1272, and by the European Union through the Horizon 2020 research and innovation program under grant 690116 (EUBra-BIGSEA Project). Results presented in this paper were obtained using the Chameleon testbed supported by the National Science Foundation.Peer Reviewe