Propuesta de un modelo de sistema de telemedicina para la atención sanitaria domiciliaria

La atención de los enfermos crónicos se está convirtiendo en un asunto de primer orden para los sistemas sanitarios de los países desarrollados, que, por su diseño, no están preparados para hacer frente a la demanda que dichos enfermos generan. El número de enfermos crónicos afectados por distintas patologías (diabetes, EPOC, insuficiencia cardiaca congestiva, SIDA) está en aumento y su esperanza de vida "a pesar de la enfermedad" crece, por lo que el coste de su atención de acuerdo con los parámetros actuales no es sostenible a medio plazo. En los últimos años están surgiendo distintas iniciativas para remodelar el tipo de atención que se presta a los enfermos crónicos. Estos trabajos proponen diferentes acciones, entre las que están la atención a domicilio y la intervención de un equipo multidisciplinar de profesionales sociosanitarios trabajando de manera coordinada, habiéndose demostrado que proporcionan importantes beneficios tanto en la calidad del cuidado como en su coste. Las tecnologías de la información y las comunicaciones pueden facilitar notablemente la atención de los enfermos conforme a estos nuevos paradigmas de cuidado. La principal aportación de esta tesis doctoral es la definición de un Modelo de Sistema de Telemedicina para el Cuidado Domiciliario de Enfermos Crónicos que contempla la prestación de servicios de manera integrada, propicia un estilo de atención centrado en el enfermo y su domicilio, y facilita el cuidado compartido de los pacientes. Se ha seleccionado el lenguaje unificado de modelado (UML) para la descripción del modelo por ser un lenguaje formal que se adapta a los objetivos perseguidos con el Modelo y aporta un nivel de abstracción adecuado para la definición funcional de los servicios de telemedicina. Para validar el Modelo se ha realizado un experimento consistente en la materialización de un sistema de telemedicina conforme al Modelo y su instalación y evaluación en un entorno de uso real. El sistema se ha desarrollado en el Grupo de Bioingeniería y Telemedicina de la Universidad Politécnica de Madrid, y la evaluación, para la que se ha elegido a pacientes con enfermedad pulmonar obstructiva crónica, se ha llevado a cabo en el Hospital Clínico de Barcelona. Este experimento ha permitido contrastar la buena adecuación de los servicios del Modelo propuesto a las necesidades de pacientes y profesionales e identificar algunos cambios deseables en dicho Modelo. Además ha permitido caracterizar el uso que se ha hecho del sistema de telemedicina y conocer tanto su aceptación entre los usuarios, como su impacto en la provisión de servicios sanitarios y en la salud de los pacientes

Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors

Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network) and SVM (Support Vector Machines), within the framework of HMM (Hidden Markov Model) in order to tackle the task of activity recognition in a home setting. The output scores of the discriminative models, after processing, are used as observation probabilities of the hybrid approach. We evaluate our approach by comparing these hybrid models with other classical activity recognition methods using five real datasets. We show how the hybrid models achieve significantly better recognition performance, with significance level p<0 : 0 5, proving that the hybrid approach is better suited for the addressed domain.This work has been supported by the Ambient Assisted Living Programme (Joint Initiative by the European Commission and EU Member States) under the Trainutri (Training and nutrition senior social platform) Project (AAL-2009-2-129) and by the Spanish Government under i-Support (Intelligent Agent Based Driver Decision Support) Project (TRA2011-29454-C03-03)

Mining Disease Courses across Organizations: A Methodology Based on Process Mining of Diagnosis Events Datasets

Berlín (Alemania) (23-27 julio 2019)This work was supported in part by grants TRA2015-63708-R and TRA2016-78886-C3-1-R (Spanish Government) and Topus (Madrid Regional Government)

Telemonitoring systems interoperability challenge: an updated review of the applicability of ISO/IEEE 11073 standards for Interoperability in telemonitoring

Proceeding of: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. EMBS 2007, Lyon, France, 22-26 August, 2007.Advances in Information and Communication Technologies, ICT, are bringing new opportunities and use cases in the field of systems and Personal Health Devices used for the telemonitoring of citizens in Home or Mobile scenarios. At a time of such challenges, this review arises from the need to identify robust technical telemonitoring solutions that are both open and interoperable. These systems demand standardized solutions to be cost effective and to take advantage of standardized operation and interoperability. Thus, the fundamental challenge is to design plug-&-play devices that, either as individual elements or as components, can be incorporated in a simple way into different Telecare systems, perhaps configuring a personal user network. Moreover, there is an increasing market pressure from companies not traditionally involved in medical markets, asking for a standard for Personal Health Devices, which foresee a vast demand for telemonitoring, wellness, Ambient Assisted Living (AAL) and ehealth applications. However, the newly emerging situations imply very strict requirements for the protocols involved in the communication. The ISO/IEEE 11073 family of standards is adapting and moving in order to face the challenge and might appear the best positioned international standards to reach this goal. This work presents an updated survey of these standards, trying to track the changes that are being fulfilled, and tries to serve as a starting-point for those who want to familiarize themselves with them.This research work has been partially supported by projects TSI2005-07068-C02-01 and TSI2004-04940-C02-01 from Ministerio de Educación y Ciencia (Spanish Government), and a personal grant to M. Galarraga from Navarre Regional Government

Point of care medical device communication standars (ISO11073/IEEE1073) in patient telemonitoring

Proceeding of: European Medical and Biological Engineering and IFMBE Conference (EMBEC 2005). November 20-25, 2005. Prague, Czech Republic.This paper reviews the use of ISO11073/ IEEE1073 international standard in patient telemonitoring. The purpose of this family of standards is to allow interoperability between medical instrumentation devices and medical information systems. Its application in the field of telemonitoring can encourage telemedicine services and e-care, preventing failures and problems that are making difficult its spread (use problems, high costs of reconfigurations and actualizations). An application guide for the system engineer that want to apply them is proposed, showing the steps to follow, the benefits and handicaps in the standard implementation for different telemonitoring scenarios. The study also includes the conformity levels that have to be fulfilled, the main application points of the standard.This work was supported by projects G03/117 from Fondo de Investigaciones Sanitarias (Spanish Government) and 41/2003 from Departamento de Salud (Navarra Regional Government), and a personal grant to Miguel Galarraga from Departamento de Salud (Navarra Regional Government).No publicad

Implementación integrada de una plataforma telemática basada en estándares para monitorización de pacientes

Proceeding of: VI Jornadas de Ingeniería Telemática (JITEL 2007), Málaga, Spain, 17-19, SeptiembreThis paper presents a proof-of-concept design of an integrated solution of a telematic platform for home telemonitoring. It is end-to-end standards-based, using ISO/IEEE11073 in the client environment and EN13606 to send the information to an Electronic Healthcare Record (EHR) server. This solution has been implemented to comply with the standards available versions and tested in a laboratory environment to demonstrate the feasibility of an end-to-end standards-based platform.Este trabajo ha recibido el apoyo de proyectos de la Comisión Interministerial de Ciencia y Tecnología (CICYT) y de los Fondos Europeos de Desarrollo Regional (FEDER) TSI2004-04940-C02-01, del VI Programa Marco (Pulsers II IP) IST-27142, y del Ministerio de Educación y Ciencia (beca FPU AP-2004-3568).No publicad

Prediction of patient evolution in terms of Clinical Risk Groups form routinely collected data using machine learning

Berlín (23-27 julio 2019)Chronicity is a problem that is affecting quality of life and increasing healthcare costs worldwide. Predictive tools can help mitigate these effects by encouraging the patients' and healthcare system's proactivity. This research work uses supervised learning techniques to build a predictive model of the healthcare status of a chronic patient, using Clinical Risk Groups (CRGs) as a measure of chronicity and prescription and diagnosis data as predictors. The model is addressed to the whole population in our healthcare system regardless of the disease, as data used are widely available in a consistent way for all patients. We explore different ways to encode data that are appropriate for machine learning. Results suggest that these data alone can be used to build accurate models, and show that, in our set, prescription information has a higher predictive value than diagnosis.This work was supported in part by projects TRA2015-63708-R and TRA2016-78886-C3-1-R (Spanish Government) and Predict-TB (European Union, Innovative Medicines Initiative)

Predicting the outcome of patients with subarachnoid hemorrhage using machine learning techniques

Background: Outcome prediction for subarachnoid hemorrhage (SAH) helps guide care and compare global management strategies. Logistic regression models for outcome prediction may be cumbersome to apply in clinical practice. Objective: To use machine learning techniques to build a model of outcome prediction that makes the knowledge discovered from the data explicit and communicable to domain experts. Material and methods: A derivation cohort (n = 441) of nonselected SAH cases was analyzed using different classification algorithms to generate decision trees and decision rules. Algorithms used were C4.5, fast decision tree learner, partial decision trees, repeated incremental pruning to produce error reduction, nearest neighbor with generalization, and ripple down rule learner. Outcome was dichotomized in favorable [Glasgow outcome scale (GOS) = I–II] and poor (GOS = III–V). An independent cohort (n = 193) was used for validation. An exploratory questionnaire was given to potential users (specialist doctors) to gather their opinion on the classifier and its usability in clinical routine. Results: The best classifier was obtained with the C4.5 algorithm. It uses only two attributes [World Federation of Neurological Surgeons (WFNS) and Fisher’s scale] and leads to a simple decision tree. The accuracy of the classifier [area under the ROC curve (AUC) = 0.84; confidence interval (CI) = 0.80–0.88] is similar to that obtained by a logistic regression model (AUC = 0.86; CI = 0.83–0.89) derived from the same data and is considered better fit for clinical use.This work was supported in part by the Spanish Ministries of Science under Grant TRA2007-67374-C02-02 and Health under Grant FIS PI 070152. The work of A. Lagares and J.F. Alen was supported by the Fundación Mutua Madrileña

PERSEIA: a biomedical wireless sensor network to support healthcare delivery for the elderly and chronically Ill

Proceeding of: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006. EMBS ´06, New York, Aug. 30 2006-Sept. 3, 2006This paper presents a system based on Ambient Intelligence (AmI) to foster home care monitoring of senior citizens and chronically ill patients. The most important fact addressed in this research is the development of non intrusive and easy to use sensing devices. According to this, medical tests do not need user collaboration to perform them, neither powering on and off the sensor, starting the measure, configuring communications, etc.This work was supported in part by the Spanish Comunidad de Madrid under Grant GR/SAL/0277/2004.Publicad