Applying IRON to a Virtual Community Scenario

Normative systems (norms) have been widely proposed as a technique for coordinating multi-agent systems (MAS). The automated synthesis of norms is a complex problem that remains open. IRON (Intelligent Robust On-line Norm synthesis mechanism) is a novel mechanism for the on-line automated synthesis of norms for MASs. IRON produces conflict-free norms that characterise necessary conditions for coordination, without over-regulation. In the past, IRON successfully regulated a traffic scenario even in the presence of non-compliant agents. In this paper, we apply IRON to synthesise norms for a virtual community scenario, where agents are users that share contents within the community. As a result, IRON synthesises norms that prevent users from uploading undesirable contents (i.e., those that users complain about). © 2013 The authors and IOS Press. All rights reserved.This work was funded by AT (CONSOLIDER CSD2007-0022), EVE (TIN2009-14702-C02-01/02), COR (TIN2012-38876-C02-01/02), MECER (201250E053) and the Generalitat of Catalunya (2009-SGR-1434).Peer Reviewe

Extending NormLab to Spur Research on Norm Synthesis

On-line norm synthesis is a widely used approach to facilitate coordination in MASs. In [2] we introduced NormLab, a computational framework to support research on on-line norm synthesis. That framework provides functionalities to model, simulate and analyse norm synthesis algorithms in an agent-based simulation environment. Here we present several extensions to that work, providing a benchmark for research on norm synthesis in MAS.Work funded by projects AT (CSD2007-0022), COR (TIN2012-38876-C02-01/02), and 2009-SGR-1434. Mike Wooldridge was supported by the ERC under Advanced Grant 291528 (“RACE").Peer reviewe

Subacute stroke physical rehabilitation evidence in activities of daily living outcomes

Supplemental Digital Content is available in the text Stroke is a leading cause of disabilities worldwide. One of the key disciplines in stroke rehabilitation is physical therapy which is primarily aimed at restoring and maintaining activities of daily living (ADL). Several meta-analyses have found different interventions improving functional capacity and reducing disability. To systematically evaluate existing evidence, from published systematic reviews of meta-analyses, of subacute physical rehabilitation interventions in (ADLs) for stroke patients. Umbrella review on meta-analyses of RCTs ADLs in MEDLINE, Web of Science, Scopus, Cochrane, and Google Scholar up to April 2018. Two reviewers independently applied inclusion criteria to select potential systematic reviews of meta-analyses of randomized controlled trials (RCTs) of physical rehabilitation interventions (during subacute phase) reporting results in ADLs. Two reviewers independently extracted name of the 1st author, year of publication, physical intervention, outcome(s), total number of participants, and number of studies from each eligible meta-analysis. The number of subjects (intervention and control), ADL outcome, and effect sizes were extracted from each study. Fifty-five meta-analyses on 21 subacute rehabilitation interventions presented in 30 different publications involving a total of 314 RCTs for 13,787 subjects were identified. Standardized mean differences (SMDs), 95% confidence intervals (fixed and random effects models), 95% prediction intervals, and statistical heterogeneity (I 2 and Q test) were calculated. Virtual reality, constraint-induced movement, augmented exercises therapy, and transcranial direct current stimulation interventions resulted statistically significant (P 0.8) but with considerable heterogeneity (I2 > 75%). Only acupuncture reached “suggestive” level of evidence. Despite the range of interventions available for stroke rehabilitation in subacute phase, there is lack of high-quality evidence in meta-analyses, highlighting the need of further research reporting ADL outcomes

Moderación automática en comunidades virtuales

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2013, Director: Maite López SánchezIn the last decade, the number of the virtual communities, users and contents has multiplied. Due to this growth, appears a problem about the control of the content and the users of these virtual communities: huge number of the contents. This fact makes more problems to control the content because normally the content is moderate with human moderators. The human moderators sometimes are users and other times are employees but in both case it’s a time or money cost. This general project would be a solution to reduce the cost of moderate full contents with automatic generation of norms and moderate the content with these norms

Handling Missing Data in Clinical Decision Support

Decidir quins són els millors tractaments és una tasca complexa quan els pacients pateixen múltiples problemes i quan un equip multidisciplinari està involucrat en la intervenció. Sempre hi ha més d’una opció de tractament i els resultats a vegades es poden veure en un període curt o un cop finalitzat el tractament. En aquest context, el disseny de sistemes eficaços de suport a la decisió clínica (CDSS) per ajudar als metges a seleccionar les intervencions més apropiades segueix sent avui dia un desafiament. La quantitat de dades disponibles no sempre és la mateixa per a tots els pacients, especialment en les fases primerenques del tractament, dificultant la inferència en els CDSS. Per millorar les capacitats dels CDSS, es proposen diferents components per als tractaments de llarga durada. Un primer component se centra a millorar la qualitat de les inferències en les dades desconegudes. L’algoritme d’imputació múltiple dinàmica (DMI) es presenta com una metodologia eficaç per a la millora de les dades. DMI és capaç d’adaptar-se a diferents escenaris amb un percentatge alt o baix de dades desconegudes. Els experiments realitzats revelen que DMI és especialment competitiu en problemes de regressió. Un segon component està dedicat a compensar les mesures de confiança, donada la incertesa associada a la informació desconeguda, incorporant mesures d’Informació Mútua en les confiances existents. El tercer component, basat en un algorisme de detecció de comunitats està orientat a trobar relacions entre decisions clíniques que no són explícites. Finalment, per il·lustrar l’aplicabilitat dels diferents components proposats, es presenten dos casos d’ús clínics reals. Un en el context hospitalari i un altre en el context del domicili.Decidir cuáles son los mejores tratamientos es una tarea compleja cuando los pacientes sufren múltiples problemas y cuando un equipo multidisciplinario está involucrado en la intervención. Siempre hay más de una opción de tratamiento y los resultados a veces se pueden ver en un período corto o al final, una vez finalizado el tratamiento.En este contexto, el diseño de sistemas eficaces de soporte a la decisión clínica (CDSS) para ayudar a los médicos a seleccionar las intervenciones más apropiadas sigue siendo hoy en día un desafío. La cantidad de datos disponibles no siempre es la misma para todos los pacientes, especialmente en las fases tempranas del tratamiento, lo que dificulta la inferencia en los CDSS. Para mejorar las capacidades de los CDSS, se proponen diferentes componentes para tratamientos a largo plazo. Un primer componente se centra en mejorar la calidad de las inferencias en los datos desconocidos. El algoritmo de imputación múltiple dinámica (DMI) se presenta como un metodología eficaz para la mejora de los datos. DMI es capaz de adaptarse a diferentes escenarios con un porcentaje alto o bajo de datos desconocidos. Los experimentos realizados revelan que DMI es especialmente competitivo en problemas de regresión. Un segundo componente está dedicado a compensar las medidas de confianza, dada la incertidumbre asociada a la información desconocida, incorporando medidas de Información Mutua en las confianzas existentes. El tercer componente basado en un algoritmo de detección de comunidades esta orientado a encontrar relaciones entre decisiones clínicas que no son explícitas. Finalmente, para ilustrar la aplicabilidad de los diferentes componentes propuestos, se presentan dos casos de uso clínico reales. Uno en el contexto hospitalario y otro en el contexto del domicilio.Deciding which are the best treatments is a complex task when patients suffer multiple impairments and when a multidisciplinary team is involved in the intervention. There is always more than a unique treatment option and the results sometimes can be viewed in a short period or only be capable to be measured when the treatment is finished. In this context, the design of effective Clinical Decision Support Systems (CDSS) to help clinicians to select most appropriate interventions is still a challenge. The amount of available data is not always the same for all patients, especially in early treatment stages, hindering the inference in CDSS. To improve the capabilities of CDSS, different components are proposed within a CDSS framework for long-term treatments. A first component is focused on improving the quality of the inferences in missing data scenarios. The Dynamic Multiple Imputation (DMI) algorithm is presented as an effective methodology for data enhancement in CDSS. DMI is capable to adapt to different scenarios with a low or high percentage of missing data. Several experiments conducted reveal that DMI is competitive with regression problems. A second component is devoted to weigh confidence measures, given the uncertainty associated to missing information, by incorporating Mutual Information measures in confidence existing estimators. A third component, based on a community detection algorithm, is proposed to find relationships between clinical decisions that are not explicit. Finally, to illustrate the applicability of different proposed components, two real clinical use cases with chronic patients are presented. The first in the hospital context and the other in the home context.Universitat Autònoma de Barcelona. Programa de Doctorat en Informàtic

Norms crowdsourcing

The aim of this Master Thesis is to define a method to support users with the co-creation of their own norms in those virtual social communities where they belong. In this sense, a dedicated norm argumentation method is proposed and used to structure and facilitate users' interaction

Norms crowdsourcing

The aim of this Master Thesis is to define a method to support users with the co-creation of their own norms in those virtual social communities where they belong. In this sense, a dedicated norm argumentation method is proposed and used to structure and facilitate users' interaction

Norms crowdsourcing

The aim of this Master Thesis is to define a method to support users with the co-creation of their own norms in those virtual social communities where they belong. In this sense, a dedicated norm argumentation method is proposed and used to structure and facilitate users' interaction

Using community detection techniques to discover non-explicit relationships in neurorehabilitation treatments

The interaction between patients and professionals in complex clinical domains, as in the case of Neurorehabilitation, is always a complex process where crucial decision making in a short period of time is required, and where every decision has a serious impact on the patient. In this situation, deciding which are the most appropriate interventions is not an easy task because these patients simultaneously present several impairments, multiple diagnoses, and required complex interdisciplinary approaches. In this context, a methodology and a tool based on ICF have been developed to explore the relationships between patient impairments and therapeutic goals. The proposed approach, based on graph analysis, was used to analyze a set of 1960 patients that suffered an Acquired Brain Injury. Results achieved show that the proposed methodology is able to find non-explicit relationships. This study constitute a first step to the goal of designing a clinical decision support tool for neurorehabilitation.Peer reviewe

NormLab: A Framework to Support Research on Norm Synthesis (Demonstration)

MAS research has investigated norms as a means to coordinate open multi-agent systems (MAS). This has spurred a strand of research on on-line norm synthesis algorithms for MASs. However, to the best of our knowledge, currently there is no computational framework to support the development and study of on-line norm synthesis. Here we present NORMLAB, a novel framework to support norm synthesis research, highlighting its important features. We also outline the operation of two novel on-line norm synthesis strategies, which significantly outperform the state of the art