Data science, analytics and artificial intelligence in e-health : trends, applications and challenges

Acknowledgments. This work has been partially supported by the Divina Pastora Seguros company.More than ever, healthcare systems can use data, predictive models, and intelligent algorithms to optimize their operations and the service they provide. This paper reviews the existing literature regarding the use of data science/analytics methods and artificial intelligence algorithms in healthcare. The paper also discusses how healthcare organizations can benefit from these tools to efficiently deal with a myriad of new possibilities and strategies. Examples of real applications are discussed to illustrate the potential of these methods. Finally, the paper highlights the main challenges regarding the use of these methods in healthcare, as well as some open research lines

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Crisis and post-crisis urban gardening initiatives from a Southern European perspective: the case of Barcelona

Throughout the 20th century, urban gardening in central and northern Europe as well as in North America has received a great deal of academic attention. However, the recent proliferation of urban gardening in other geographies, such as southern Europe in the aftermath of the economic crisis of 2007-2008, remains underexplored. The economic crisis put on hold urban developments in many southern European cities, leaving idle plots of land waiting to be urbanized. The crisis also triggered radical political demands, such as those of the Indignados, as well as fuelling narratives revolving around social entrepreneurship and social innovation. Barcelona emerges as a laboratory of urban gardening initiatives in vacant lots mobilizing either radical urban demands or embedding new post-crisis rhetoric around social entrepreneurship. Through a combination of qualitative methods, including participant observation, a literature review, semi-structured interviews, informal conversations and field diaries, we present a characterization and evolution of the three most prominent urban gardening initiatives in the city of Barcelona (including 54 gardens at the end of 2016): the Network of Municipal Gardens (municipally led gardens for retired people); the Network of Communitarian Gardens (social movements); and the Empty Plots Plan (social entrepreneurial urban gardening). Subsequently, we discuss the different meanings of gardening in crisis/post-crisis Barcelona as well as the urban politics that each initiative articulates. Our results show that urban gardens within the city are an expression of different and non-exclusive meanings that explicitly or implicitly, in a context of crisis and post-crisis, mobilize notions of political gardening

Minería de datos educativos y análisis de datos sobre aprendizaje: diferencias, parecidos y evolución en el tiempo

Technological progress in recent decades has enabled people to learn in different ways. Universities now have more educational models to choose from, i.e., b-learning and e-learning. Despite the increasing opportunities for students and instructors, online learning also brings challenges due to the absence of direct human contact. Online environments allow the generation of large amounts of data related to learning/teaching processes, which offers the possibility of extracting valuable information that may be employed to improve students¿ performance. In this paper, we aim to review the similarities and differences between Educational Data Mining and Learning Analytics, two relatively new and increasingly popular fields of research concerned with the collection, analysis, and interpretation of educational data. Their origins, goals, differences, similarities, time evolution, and challenges are addressed, as are their relationship with Big Data and MOOCs.El progreso tecnológico de las últimas décadas ha hecho posible una diversidad de formas de aprendizaje. Hoy en día las universidades ofrecen múltiples modelos de enseñanza entre los que poder elegir, por ejemplo aprendizaje mixto (b-learning) o aprendizaje electrónico. Aunque cada vez son más numerosas las oportunidades para alumnos y profesores, el aprendizaje en línea también plantea dificultades debidas a la falta de contacto humano directo. Los entornos en línea permiten generar grandes cantidades de datos relacionados con los procesos de enseñanza-aprendizaje, de los que se puede extraer una valiosa información que se puede usar para mejorar el desempeño del alumnado. En este trabajo queremos estudiar los parecidos y diferencias entre la minería de datos educativos y el análisis de datos sobre aprendizaje, dos campos de investigación relativamente nuevos y crecientemente populares relacionados con la recogida, el análisis y la interpretación de datos educativos. Trataremos su origen, objetivos, diferencias y parecidos, evolución en el tiempo y retos a los que se enfrentan, así como su relación con los macrodatos y los cursos en línea abiertos y masivos (MOOC).El progrés tecnològic de les darreres dècades ha fet possible una diversitat de formes d'aprenentatge. Avui dia les universitats ofereixen múltiples models d'ensenyament entre els quals podem triar, per exemple, l'aprenentatge mixt (b-learning) o l¿aprenentatge electrònic. Si bé cada cop són més nombroses les oportunitats per a alumnes i professors, l'aprenentatge en línia també planteja dificultats degudes a la manca de contacte humà directe. Els entorns en línia permeten que es generin grans quantitats de dades relacionades amb els processos d'ensenyament i aprenentatge, de les quals es pot extreure una valuosa informació que es pot fer servir per millorar l'actuació de l'alumnat. En aquest treball volem estudiar les semblances i diferències entre la mineria de dades educatives i l'anàlisi de dades sobre l'aprenentatge, dos camps de recerca relativament nous i creixentment populars relacionats amb la recollida, l'anàlisi i la interpretació de dades sobre educació. En tractarem l'origen, els objectius, les diferències i semblances, l'evolució que han tingut en el temps i els reptes a què s'enfronten, així com la seva relació amb les dades massives i els cursos en línia oberts i massius (MOOC)

A biased-randomized iterated local search algorithm for rich portfolio optimization

This research develops an original algorithm for rich portfolio optimization (ARPO), considering more realistic constraints than those usually analyzed in the literature. Using a matheuristic framework that combines an iterated local search metaheuristic with quadratic programming, ARPO efficiently deals with complex variants of the mean-variance portfolio optimization problem, including the well-known cardinality and quantity constraints. ARPO proceeds in two steps. First, a feasible initial solution is constructed by allocating portfolio weights according to the individual return rate. Secondly, an iterated local search framework, which makes use of quadratic programming, gradually improves the initial solution throughout an iterative combination of a perturbation stage and a local search stage. According to the experimental results obtained, ARPO is very competitive when compared against existing state-of-the-art approaches, both in terms of the quality of the best solution generated as well as in terms of the computational times required to obtain it. Furthermore, we also show that our algorithm can be used to solve variants of the portfolio optimization problem, in which inputs (individual asset returns, variances and covariances) feature a random component. Notably, the results are similar to the benchmark constrained efficient frontier with deterministic inputs, if variances and covariances of individual asset returns comprise a random component. Finally, a sensitivity analysis has been carried out to test the stability of our algorithm against small variations in the input data

Analyzing students' perceptions to improve the design of an automated assessment tool in online distributed programming

Designing an automated assessment tool in online distributed programming can provide students with a meaningful distributed learning environment that improves their academic performance. However, it is a complex and challenging endeavor that, as far as we know, has not been investigated yet. To address this research gap, this work presents a new automated assessment tool in online distributed programming, called DSLab. The tool was evaluated in a real long-term online educational experience by analyzing students' perceptions with the aim of improving its design. A quantitative analysis method was employed to collect and analyze data concerning students perceptions as to whether using the DSLab tool was really a worthwhile experience. Our study shows that the DSLab tool includes acceptable utility and efficiency features. It also identifies factors that influence current design efficiency with the aim of improving DSLab design by suggesting new functionalities and ideas

Editorial of special issue on: Applications of risk analysis and analytics in engineering and economics

In areas such as engineering, economics, and insurance, real-world systems are becoming increasingly complex to analyse due to their global scale as well as to the uncertainty and dynamic conditions that characterises realistic scenarios. This increasing complexity makes risk analysis and analytic (RA&A) methods more important than ever, since being able to design, develop, and operate real-live systems while assessing and reducing their risk of malfunctions or inefficiencies constitutes one of the most relevant challenges in our current society. RA&A methods and techniques have rapidly evolved over the last years. One factor that explains this development is outstanding and continuous improvement in software and computing power, which facilitates the use of hybrid algorithms combining risk/reliability principles with modern optimisation and simulation frameworks. Another factor is the increasing use of problem solving approaches that benefit from the so-called "big data" phenomenon. However, despite these significant advances in this scientific arena, there seems to be an important gap between theory and practice; most industrial sectors (including engineering, economics, and insurance) are only starting to employ the full potential of state-of-the-art scientific advances in RA&A

A simheuristic approach for freight transportation in smart cities

In modern society, sustainable transportation practices in smart cities are becoming increasingly important for both companies and citizens. These practices constitute a global trend, which affects multiple sectors resulting in relevant socio-economic and environmental challenges. Moreover, uncertainty plays a crucial role in transport activities; for instance, travel time may be affected by road work, the weather, or accidents, among others. This paper addresses a rich extension of the capacitated vehicle routing problem, which considers sustainability indicators (i.e., economic, environmental and social impacts) and stochastic traveling times. A simheuristic approach integrating Monte Carlo simulation into a multi-start metaheuristic is proposed to solve it. A computational experiment is carried out to validate our approach, and analyze the trade-off between sustainability dimensions and the effect of stochasticity on the solutions

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer's willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.Este artículo revisa la literatura existente sobre la combinación de metaheurísticas con métodos de aprendizaje automático y luego introduce el concepto de heurística de aprendizaje, un tipo novedoso de algoritmos híbridos. Las técnicas de aprendizaje se pueden usar para resolver problemas combinatorios de optimización con entradas dinámicas (COPDI). En estos COPDI, las entradas problemáticas (elementos ubicados ya sea en la función objetivo o en el conjunto de restricciones) no se fijan de antemano como de costumbre. Por el contrario, pueden variar de forma predecible (no aleatoria) ya que la solución se construye parcialmente de acuerdo con algún proceso iterativo basado en heurística. Por ejemplo, la disposición de un consumidor a gastar en un producto específico puede cambiar a medida que disminuye la disponibilidad de este producto y aumenta su precio. Por lo tanto, estas entradas pueden tomar diferentes valores dependiendo de la configuración de la solución actual. Estas variaciones en las entradas pueden requerir una coordinación entre el mecanismo de aprendizaje y el algoritmo metaheurístico: en cada iteración, el método de aprendizaje actualiza el modelo de entradas utilizado por la metaheurística.Aquest article revisa la literatura existent sobre la combinació de metaheurístiques amb mètodes d'aprenentatge automàtic i després introdueix el concepte d'heurística d'aprenentatge, un tipus nou d'algorismes híbrids. Les tècniques d'aprenentatge es poden usar per resoldre problemes combinatoris d'optimització amb entrades dinàmiques (COPDI). En aquests COPDI, les entrades problemàtiques (elements ubicats ja sigui en la funció objectiu o en el conjunt de restriccions) no es fixen per endavant com de costum. Per contra, poden variar de forma predictible (no aleatòria) ja que la solució es construeix parcialment d'acord amb algun procés iteratiu basat en heurística. Per exemple, la disposició d'un consumidor a gastar en un producte específic pot canviar a mesura que disminueix la disponibilitat d'aquest producte i augmenta el preu. Per tant, aquestes entrades poden prendre diferents valors depenent de la configuració de la solució actual. Aquestes variacions en les entrades poden requerir una coordinació entre el mecanisme d'aprenentatge i l'algoritme metaheurístic: en cada iteració, el mètode d'aprenentatge actualitza el model d'entrades utilitzat per la metaheurística

Editorial of special issue on: Applications of risk analysis and analytics in engineering and economics

In areas such as engineering, economics, and insurance, real-world systems are becoming increasingly complex to analyse due to their global scale as well as to the uncertainty and dynamic conditions that characterises realistic scenarios. This increasing complexity makes risk analysis and analytic (RA&A) methods more important than ever, since being able to design, develop, and operate real-live systems while assessing and reducing their risk of malfunctions or inefficiencies constitutes one of the most relevant challenges in our current society. RA&A methods and techniques have rapidly evolved over the last years. One factor that explains this development is outstanding and continuous improvement in software and computing power, which facilitates the use of hybrid algorithms combining risk/reliability principles with modern optimisation and simulation frameworks. Another factor is the increasing use of problem solving approaches that benefit from the so-called "big data" phenomenon. However, despite these significant advances in this scientific arena, there seems to be an important gap between theory and practice; most industrial sectors (including engineering, economics, and insurance) are only starting to employ the full potential of state-of-the-art scientific advances in RA&A