Contributions to interoperability, scalability and formalization of personal health systems

The ageing of the world's population combined with unhealthy lifestyles are contributing to a major prevalence of chronic diseases. This scenario poses the challenge of providing good healthcare services to that people affected by chronic illnesses, but without increasing its costs. A prominent way to face this challenge is through pervasive healthcare. Research in pervasive healthcare tries to shift the current centralized healthcare delivery model focused on the doctors, to a more distributed model focused on the patients. In this context Personal Health Systems (PHSs) consists on approaching sampling technologies into the hands of the patients, without disturbing its activities of the daily life, to monitor patient's physiological parameters and providing feedback on their state. The use of PHSs involves the patients in the management of their illness and in their own well being too. The development of PHSs has to face technological issues in order to be accepted by our society. Within them it is important to ensure interoperability between different systems in order to make them work together. Scalability it is also a concern, as their performance must not decrease when increasing the number of users. Another issue is how to formalize the medical knowledge for each patient, as different patients may have different target goals. Security and privacy are a must feature because of the sensitive nature of medical data. Other issues involve the the integration with legacy systems, and the usability of graphical user interfaces in order to encourage old people with the use these technologies. The aim of this PhD thesis is to contribute into the state-of-the-art of PHSs by tackling together different of the above-mentioned challenges. First, to achieve interoperability we use the CDA standard as a format to encode and exchange health data and alerts related with the status of the patient. We show how these documents can be generated automatically through the use of XML templates. Second, we address the scalability by distributing the computations needed to monitor the patients over their devices, rather than performing them in a centralized server. In this context we develop the MAGPIE agent platform, which runs on Android devices, as a framework able to provide intelligence to PHSs, and generate alerts that can be of interest for the patients and the medical doctors. Third, we focus on the formalization of PHSs by providing a tool for the practitioners where they can define, in a graphical way, monitoring rules related with chronic diseases that are integrated with the MAGPIE agent platform. The thesis also explores different ways to share the data collected with PHSs in order to improve the outcomes obtained with the use of this technology. Data is shared between individuals following a Distributed Event-Based System (DEBS) approach, where different people can subscribe to the alerts produced by the patient. Data is also shared between institutions with a network protocol called MOSAIC, and we focus on the security aspects of this protocol. The research in this PhD focuses in the use case of Diabetes Mellitus; and it has been developed in the context of the projects MONDAINE, MAGPIE, COMMODITY12 and TAMESIS.L'envelliment de la població mundial combinat amb uns estils de vida no saludables contribueixen a una major prevalença d'enfermetats cròniques. Aquest escenari presenta el repte de proporcionar uns bons serveis sanitaris a les persones afectades per aquestes enfermetats, sense incrementar-ne els costos. Una solució prometedora a aquest repte és mitjançant l'aplicació del que en anglès s'anomena "pervasive healthcare". L'investigació en aquesta camp tracta de canviar l'actual model centralitzat de serveis sanitaris enfocat en el personal sanitari, per un model de serveis distribuït enfocat en els pacients. En aquest context, els Personal Health Systems (PHSs) consisteixen en posar a l'abast dels pacients les tecnologies de monitorització, i proporcionar-los informació sobre el seu estat. L'ús de PHSs involucra els pacients en la gestió de la seva enfermetat i del seu propi benestar. L'acceptació dels PHSs per part de la societat implica certs reptes tecnològics en el seu desenvolupament. És important garantir la seva interoperabilitat per tal de que puguin treballar conjuntament. La seva escalabilitat també s'ha de tenir en compte, ja que el seu rendiment no s'ha de veure afectat al incrementar-ne el número d'usuaris. Un altre aspecte a considerar és com formalitzar el coneixement mèdic per cada pacient, ja que cada un d'ells pot tenir objectius diferents. La seguretat i privacitat són característiques desitjades degut a la naturalesa sensible de les dades mèdiques. Altres problemàtiques impliquen la integració amb sistemes heretats, i la usabilitat de les interfícies gràfiques per fomentar-ne el seu ús entre les persones grans. L'objectiu d'aquesta tesi és contribuir a l'estat de l'art dels PHSs tractant de manera conjunta varis dels reptes mencionats. Per abordar l'interoperabilitat s'utilitza l'estàndard CDA com a format per codificar les dades mèdiques i alertes relacionades amb el pacient. A més es mostra com aquests documents poden generar-se de forma automàtica mitjançant l' ús de plantilles XML. Per tractar l'escalabilitat es distribueixen les computacions per monitoritzar els pacients entre els seus terminals mòbils, en comptes de realitzar-les en un servidor central. En aquest context es desenvolupa la plataforma d'agents MAGPIE com a framework per proporcionar intelligència als PHSs i generar alertes d'interès per al metge i el pacient. La formalització s'aborda mitjançant una eina que permet als metges definir de manera gràfica regles de monitorització relacionades amb enfermetats cròniques, que a més estan integrades amb la plataforma d'agents MAGPIE. La tesi també explora diferents maneres de compartir les dades recol·lectades amb un PHS, amb l'objectiu de millorar els resultats obtinguts amb aquesta tecnologia. Les dades es comparteixen entre individus seguint un enfoc de sistemes distribuïts basats en events (DEBS), on diferents usuaris poden subscriure's a les alertes produïdes per el pacient. Les dades també es comparteixen entre institucions mitjançant un protocol de xarxa anomenat MOSAIC. A la tesi es desenvolupen els aspectes de seguretat d'aquest protocol. La test es centra en la Diabetis Mellitus com a cas d'ús, i s'ha realitzat en el context dels projectes MONDAINE, MAGPIE, COMMODITY12 i TAMESIS.El envejecimiento de la población mundial combinado con unos estilos de vida no saludables contribuyen a una mayor prevalencia de enfermedades crónicas. Este escenario presenta el reto de proporcionar unos buenos servicios sanitarios a las personas afectadas por estas enfermedades, sin incrementar sus costes. Una solución prometedora a este reto es mediante la aplicación de lo que en inglés se denomina "pervasive healthcare". La investigación en este campo trata de cambiar el actual modelo centralizado de servicios sanitarios enfocado hacia el personal sanitario, por un modelo distribuido enfocado hacia los pacientes. En este contexto, los Personal Health Systems (PHSs) consisten en poner al alcance de los pacientes las tecnologías de monitorización, y proporcionarles información sobre su estado. El uso de PHSs involucra a los pacientes en la gestión de su enfermedad y en su propio bienestar. La aceptación de los PHSs por parte de la sociedad implica ciertos retos tecnológicos en su desarrollo. Es importante garantizar su interoperabilidad para que puedan trabajar conjuntamente. Su escalabilidad también se debe tener en cuenta, ya que su rendimiento no tiene que verse afectado al incrementar su número de usuarios. Otro aspecto a considerar es cómo formalizar el conocimiento médico para cada paciente, ya que cada uno puede tener objetivos distintos. La seguridad y privacidad son características deseadas debido a la naturaleza sensible de los datos médicos. Otras problemáticas implican la integración con sistemas heredados, y la usabilidad de las interfaces gráficas para fomentar su uso entre las personas mayores. El objetivo de esta tesis es contribuir al estado del arte de los PHSs tratando de manera conjunta varios de los retos mencionados. Para abordar la interoperabilidad se usa el estándar CDA como formato para codificar los datos médicos y alertas relacionados con el paciente. Además se muestra como estros documentos pueden generarse de forma automática mediante el uso de plantillas XML. Para tratar la escalabilidad se distribuye la computación para monitorizar a los pacientes en sus terminales móbiles, en lugar de realizarla en un servidor central. En este contexto se desarrolla la plataforma de agentes MAGPIE como framework para proporcionar inteligencia a los PHSs y generar alertas de interés para el médico y el paciente. La formalización se aborda mediante una herramienta que permite a los médicos definir de manera gráfica reglas de monitorización relacionadas con enfermedades crónicas, que ademas están integradas con la plataforma de agentes MAGPIE. La tesis también explora distintas formas de compartir los datos recolectados con un PHS, con el fin de mejorar los resultados obtenidos mediante esta tecnología. Los datos se comparten entre individuos siguiendo un enfoque de sistemas distribuidos basados en eventos (DEBS), donde distintos usuarios pueden suscribirse a las alertas producidas por el paciente. Los datos también se comparten entre instituciones mediante un protocolo dered llamado MOSAIC. En la tesis se desarrollan los aspectos de seguridad de este protocolo. La tesis se centra en la Diabetes Mellitus como caso de uso, y se ha realizado en el contexto de los proyectos MONDAINE, MAGPIE, COMMODITY12 y TAMESIS.Postprint (published version

Indexing the Event Calculus with Kd-trees to Monitor Diabetes

Personal Health Systems (PHS) are mobile solutions tailored to monitoring patients affected by chronic non communicable diseases. A patient affected by a chronic disease can generate large amounts of events. Type 1 Diabetic patients generate several glucose events per day, ranging from at least 6 events per day (under normal monitoring) to 288 per day when wearing a continuous glucose monitor (CGM) that samples the blood every 5 minutes for several days. This is a large number of events to monitor for medical doctors, in particular when considering that they may have to take decisions concerning adjusting the treatment, which may impact the life of the patients for a long time. Given the need to analyse such a large stream of data, doctors need a simple approach towards physiological time series that allows them to promptly transfer their knowledge into queries to identify interesting patterns in the data. Achieving this with current technology is not an easy task, as on one hand it cannot be expected that medical doctors have the technical knowledge to query databases and on the other hand these time series include thousands of events, which requires to re-think the way data is indexed. In order to tackle the knowledge representation and efficiency problem, this contribution presents the kd-tree cached event calculus (\ceckd) an event calculus extension for knowledge engineering of temporal rules capable to handle many thousands events produced by a diabetic patient. \ceckd\ is built as a support to a graphical interface to represent monitoring rules for diabetes type 1. In addition, the paper evaluates the \ceckd\ with respect to the cached event calculus (CEC) to show how indexing events using kd-trees improves scalability with respect to the current state of the art.Comment: 24 pages, preliminary results calculated on an implementation of CECKD, precursor to Journal paper being submitted in 2017, with further indexing and results possibilities, put here for reference and chronological purposes to remember how the idea evolve

Circulación univentricular. Estudio de los factores pronósticos de la evolución clínica y estudio comparativo de la evolución postoperatoria de los pacientes con o sin hipertensión pulmonar

La operación de Fontan es la cirugía que culmina la fisiología del corazón univentricular. Los pacientes en esta situación son portadores de diversos tipos de cardiopatías congénitas graves no reparables. Los objetivos de este trabajo son analizar los factores que determinan la evolución postoperatoria y la mortalidad de los pacientes sometidos a cirugía de Fontan, evaluar si la presencia de hipertensión pulmonar previa a la cirugía de Fontan condiciona la evolución postoperatoria y la mortalidad a medio-largo plazo, y estudiar si el tratamiento con vasodilatadores pulmonares en estos pacientes modifica la evolución postoperatoria inmediata y la mortalidad a largo plazo..

On the use of biased-randomized algorithms for solving non-smooth optimization problems

Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines

Knowledge sharing in the health scenario

The understanding of certain data often requires the collection of similar data from different places to be analysed and interpreted. Interoperability standards and ontologies, are facilitating data interchange around the world. However, beyond the existing networks and advances for data transfer, data sharing protocols to support multilateral agreements are useful to exploit the knowledge of distributed Data Warehouses. The access to a certain data set in a federated Data Warehouse may be constrained by the requirement to deliver another specific data set. When bilateral agreements between two nodes of a network are not enough to solve the constraints for accessing to a certain data set, multilateral agreements for data exchange are needed. We present the implementation of a Multi-Agent System for multilateral exchange agreements of clinical data, and evaluate how those multilateral agreements increase the percentage of data collected by a single node from the total amount of data available in the network. Different strategies to reduce the number of messages needed to achieve an agreement are also considered. The results show that with this collaborative sharing scenario the percentage of data collected dramaticaly improve from bilateral agreements to multilateral ones, up to reach almost all data available in the network.Peer ReviewedPostprint (published version

Knowledge sharing in the health scenario

The understanding of certain data often requires the collection of similar data from different places to be analysed and interpreted. Interoperability standards and ontologies, are facilitating data interchange around the world. However, beyond the existing networks and advances for data transfer, data sharing protocols to support multilateral agreements are useful to exploit the knowledge of distributed Data Warehouses. The access to a certain data set in a federated Data Warehouse may be constrained by the requirement to deliver another specific data set. When bilateral agreements between two nodes of a network are not enough to solve the constraints for accessing to a certain data set, multilateral agreements for data exchange are needed. We present the implementation of a Multi-Agent System for multilateral exchange agreements of clinical data, and evaluate how those multilateral agreements increase the percentage of data collected by a single node from the total amount of data available in the network. Different strategies to reduce the number of messages needed to achieve an agreement are also considered. The results show that with this collaborative sharing scenario the percentage of data collected dramaticaly improve from bilateral agreements to multilateral ones, up to reach almost all data available in the network

Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

[EN] Although knowledge of the microstructure of food of vegetal origin helps us to understand the behavior of food materials, the variability in the microstructural elements complicates this analysis. In this regard, the construction of learning models that represent the actual microstructures of the tissue is important to extract relevant information and advance in the comprehension of such behavior. Consequently, the objective of this research is to compare two machine learning techniques¿Convolutional Neural Networks (CNN) and Radial Basis Neural Networks (RBNN)¿when used to enhance its microstructural analysis. Two main contributions can be highlighted from this research. First, a method is proposed to automatically analyze the microstructural elements of vegetal tissue; and second, a comparison was conducted to select a classifier to discriminate between tissue structures. For the comparison, a database of microstructural elements images was obtained from pumpkin (Cucurbita pepo L.) micrographs. Two classifiers were implemented using CNN and RBNN, and statistical performance metrics were computed using a 5-fold cross-validation scheme. This process was repeated one hundred times with a random selection of images in each repetition. The comparison showed that the classifiers based on CNN produced a better fit, obtaining F1¿score average of 89.42% in front of 83.83% for RBNN. In this study, the performance of classifiers based on CNN was significantly higher compared to those based on RBNN in the discrimination of microstructural elements of vegetable foods.Oblitas, J.; Mejía, J.; De-La-Torre, M.; Avila-George, H.; Seguí Gil, L.; Mayor López, L.; Ibarz, A.... (2021). Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks. 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