Development of a Predictive Model for Induction Success of Labour

Induction of the labour process is an extraordinarily common procedure used in some pregnancies. Obstetricians face the need to end a pregnancy, for medical reasons usually (maternal or fetal requirements) or less frequently, social (elective inductions for convenience). The success of induction procedure is conditioned by a multitude of maternal and fetal variables that appear before or during pregnancy or birth process, with a low predictive value. The failure of the induction process involves performing a caesarean section. This project arises from the clinical need to resolve a situation of uncertainty that occurs frequently in our clinical practice. Since the weight of clinical variables is not adequately weighted, we consider very interesting to know a priori the possibility of success of induction to dismiss those inductions with high probability of failure, avoiding unnecessary procedures or postponing end if possible. We developed a predictive model of induced labour success as a support tool in clinical decision making. Improve the predictability of a successful induction is one of the current challenges of Obstetrics because of its negative impact. The identification of those patients with high chances of failure, will allow us to offer them better care improving their health outcomes (adverse perinatal outcomes for mother and newborn), costs (medication, hospitalization, qualified staff) and patient perceived quality. Therefore a Clinical Decision Support System was developed to give support to the Obstetricians. In this article, we had proposed a robust method to explore and model a source of clinical information with the purpose of obtaining all possible knowledge. Generally, in classification models are difficult to know the contribution that each attribute provides to the model. We had worked in this direction to offer transparency to models that may be considered as black boxes. The positive results obtained from both the information recovery system and the predictions and explanations of the classification show the effectiveness and strength of this tool

Diffusion Maps for dimensionality reduction and visualization of meteorological data

This is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, VOL 163, (2015) DOI 10.1016/j.neucom.2014.08.090The growing interest in big data problems implies the need for unsupervised methods for data visualization and dimensionality reduction. Diffusion Maps (DM) is a recent technique that can capture the lower dimensional geometric structure underlying the sample patterns in a way which can be made to be independent of the sampling distribution. Moreover, DM allows us to define an embedding whose Euclidean metric relates to the sample's intrinsic one which, in turn, enables a principled application of k-means clustering. In this work we give a self-contained review of DM and discuss two methods to compute the DM embedding coordinates to new out-of-sample data. Then, we will apply them on two meteorological data problems that involve time and spatial compression of numerical weather forecasts and show how DM is capable to, first, greatly reduce the initial dimension while still capturing relevant information in the original data and, also, how the sample-derived DM embedding coordinates can be extended to new patterns.The authors acknowledge partial support from Spain's grant TIN2010-21575-C02-01 and the UAM{ADIC Chair for Machine Learning. The first author is also supported by an FPI{UAM grant and kindly thanks the Applied Mathematics Department of Yale University for receiving her during her visits

Dinámica de cambio en las redes sociales y la formación de vínculos transnacionales : un caso de venezolanos profesionales viviendo en París

La teoría de redes sociales ha sido ampliamente documentada en el contexto de la migración internacional. La mayoría de estas investigaciones se han centrado en la fuerza que demuestran los vínculos de parentesco y amistad en la atracción de migrantes. Sin embargo, pocos estudios han indagado, empíricamente, los procesos de configuración de las redes y su influencia estructural sobre los procesos de movilidad territorial. El objetivo de este artículo es emplear la metodología de Análisis de Redes Sociales, para identificar propiedades estructurales que pueden estar influyendo sobre la difusión del fenómeno migratorio en Venezuela, describir la estructura reticular de la migración calificada y su evolución hacia la configuración de una posible comunidad transnacional. Para ello datos de redes personales fueron recolectados entre marzo y mayo de 2016, entre una comunidad de venezolanos profesionales residenciados en París. Los resultados confirman que las redes personales de los entrevistados poseen focos de radiación de comportamiento migratorio en Venezuela, y aunque las estructuras reticulares se muestran algo dispersas, también presentan ciertos rasgos de expansión que dejan en evidencia la configuración de una comunidad transnacional de venezolanos en París.The theory of social networks has been widely documented in the context of international migration. Most of these investigations focus on the strength shown by the ties of kinship and friendship to attract migrants. However, few studies have investigated the processes of network configuration and its structural influence on migration empirically. The objective of this article is to use the methodology of Social Network Analysis, to identify structural properties that may be influencing the spread of the migratory phenomenon in Venezuela, and to describe the reticular structure of skilled migration and its evolution towards the configuration of a possible transnational community. To do this, we collected data from personal (egocentric) networks among a community of Venezuelan living in Paris. The results confirm that the personal networks of the interviewees have the power to spread the migratory behavior in Venezuela and they show evidence of the configuration of a transnational community of Venezuelans in Paris

Debates virtuales y concepciones de estudiantes para maestro sobre resolución de problemas

Esta investigación estudia la influencia de la participación en un entorno de aprendizaje sobre las concepciones de estudiantes para maestros. El entorno de aprendizaje fue diseñado “ad hoc” integrando la resolución de tareas profesionales y la participación en “debates virtuales”. El entorno de aprendizaje proporcionó diferentes ocasiones para la interacción, la negociación de significados y la construcción de conocimiento. Del análisis de la participación en los debates, se eligió un grupo de estudiantes para maestro para realizar un estudio de caso centrado en (1) las concepciones de los estudiantes para maestro sobre la resolución de problemas como objecto de enseñanza y aprendizaje, y (2) el cambio en las concepciones. Los resultados muestran que los estudiantes para maestro consideraban la resolución de problemas como un contexto para desarrollar los algoritmos. Además se constató que estas concepciones eran dificiles de modificar aunque se pudieron observar algunos cambios

Debates virtuales y concepciones de estudiantes para maestro sobre resolución de problemas

Esta investigación estudia la influencia de la participación en un entorno de aprendizaje sobre las concepciones de estudiantes para maestros. El entorno de aprendizaje fue diseñado “ad hoc” integrando la resolución de tareas profesionales y la participación en “debates virtuales”. El entorno de aprendizaje proporcionó diferentes ocasiones para la interacción, la negociación de significados y la construcción de conocimiento. Del análisis de la participación en los debates, se eligió un grupo de estudiantes para maestro para realizar un estudio de caso centrado en (1) las concepciones de los estudiantes para maestro sobre la resolución de problemas como objeto de enseñanza y aprendizaje, y (2) el cambio en las concepciones. Los resultados muestran que los estudiantes para maestro consideraban la resolución de problemas como un contexto para desarrollar los algoritmos. Además, se constató que estas concepciones eran difíciles de modificar aunque se pudieron observar algunos cambios

Automatic tree detection and attribute characterization using portable terrestrial lidar

Currently, the implementation of portable laser scanners (PLS) in forest inventories is being studied, since they allow for significantly reduced field-work time and costs when compared to the traditional inventory methods and other LiDAR systems. However, it has been shown that their operability and efficiency are dependent upon the species assessed, and therefore, there is a need for more research assessing different types of stands and species. Additionally, a few studies have been conducted in Eucalyptus stands, one of the tree genus that is most commonly planted around the world. In this study, a PLS system was tested in a Eucalyptus globulus stand to obtain different metrics of individual trees. An automatic methodology to obtain inventory data (individual tree positions, DBH, diameter at different heights, and height of individual trees) was developed using public domain software. The results were compared to results obtained with a static terrestrial laser scanner (TLS). The methodology was able to identify 100% of the trees present in the stand in both the PLS and TLS point clouds. For the PLS point cloud, the RMSE of the DBH obtained was 0.0716, and for the TLS point cloud, it was 0.176. The RMSE for height for the PLS point cloud was 3.415 m, while for the PLS point cloud, it was 10.712 m. This study demonstrates the applicability of PLS systems for the estimation of the metrics of individual trees in adult Eucalyptus globulus stands.Agencia Estatal de Investigación | Ref. PID2019-111581RB-I00Ministerio de Ciencia, Innovación y Universidades | Ref. FPU19/02054Universidade de Vigo/CISU

Health-related preferences of older patients with multimorbidity: the protocol for an evidence map

Introduction: Interaction of conditions and treatments, complicated care needs and substantial treatment burden make patient–physician encounters involving multimorbid older patients highly complex. To optimally integrate patients’ preferences, define and prioritise realistic treatment goals and individualise care, a patient-centred approach is recommended. However, the preferences of older patients, who are especially vulnerable and frequently multimorbid, have not been systematically investigated with regard to their health status. The purpose of this evidence map is to explore current research addressing health-related preferences of older patients with multimorbidity, and to identify the knowledge clusters and research gaps. Methods and analysis: To identify relevant research, we will conduct searches in the electronic databases MEDLINE, EMBASE, PsycINFO, PSYNDEX, CINAHL, Social Science Citation Index, Social Science Citation Index Expanded and the Cochrane library from their inception. We will check reference lists of relevant articles and carry out cited reference research (forward citation tracking). Two independent reviewers will screen titles and abstracts, check full texts for eligibility and extract the data. Any disagreement will be resolved and consensus reached with the help of a third reviewer. We will include both qualitative and quantitative studies, and address preferences from the patients’ perspectives in a multimorbid population of 60 years or older. There will be no restrictions on the publication language. Data extraction tables will present study and patient characteristics, aim of study, methods used to identify preferences and outcomes (ie, type of preferences). We will summarise the data using tables and figures (ie, bubble plot) to present the research landscape and to describe clusters and gaps. Ethics and dissemination: Due to the nature of the proposed evidence map, ethics approval will not be required. Results from our research will be disseminated by means of specifically prepared materials for patients, at relevant (inter)national conferences and via publication in peer-reviewed journals

Diffusion maps and local models for wind power prediction

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-33266-1_70Proceedings of 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012In this work we will apply Diffusion Maps (DM), a recent technique for dimensionality reduction and clustering, to build local models for wind energy forecasting. We will compare ridge regression models for K–means clusters obtained over DM features, against the models obtained for clusters constructed over the original meteorological data or principal components, and also against a global model. We will see that a combination of the DM model for the low wind power region and the global model elsewhere outperforms other options.With partial support from grant TIN2010-21575-C02-01 of Spain’s Ministerio de Economía y Competitividad and the UAM–ADIC Chair for Machine Learning in Modelling and Prediction. The first author is also supported by an FPI-UAM grant and kindly thanks the Applied Mathematics Department of Yale University for receiving her during a visit. The second author is supported by the FPU-MEC grant AP2008-00167. We also thank Red Eléctrica de España, Spain’s TSO, for providing historic wind energy dat