Evaluating Patient Engagement and User Experience of a Positive Technology Intervention: The H-CIM Case

The present chapter will provide an example of an intervention evaluation from the joint viewpoints of patient engagement and user experience. The authors evaluated H-CIM, a technological platform for the intelligent monitoring of physiological data of elderly patients performing physiotherapy exercises. Descriptive quantitative measures, behavioral observation, and qualitative interviews are integrated to evaluate H-CIM ability in (1) guaranteeing a positive experience to its users and (2) supporting them in advancing through a patient engagement development. This contribution would constitute a practical example of how these fundamental factors should be considered and evaluated when implementing positive technology for healthcare

Detecting Awareness in the Vegetative State: Electroencephalographic Evidence for Attempted Movements to Command

Patients in the Vegetative State (VS) do not produce overt motor behavior to command and are therefore considered to be unaware of themselves and of their environments. However, we recently showed that high-density electroencephalography (EEG) can be used to detect covert command-following in some VS patients. Due to its portability and inexpensiveness, EEG assessments of awareness have the potential to contribute to a standard clinical protocol, thus improving diagnostic accuracy. However, this technique requires refinement and optimization if it is to be used widely as a clinical tool. We asked a patient who had been repeatedly diagnosed as VS for 12-years to try to move his left and right hands, between periods of rest, while EEG was recorded from four scalp electrodes. We identified appropriate and statistically reliable modulations of sensorimotor beta rhythms following commands to try to move, which could be significantly classified at a single-trial level. These reliable effects indicate that the patient attempted to follow the commands, and was therefore aware, but was unable to execute an overtly discernable action. The cognitive demands of this novel task are lower than those used previously and, crucially, allow for awareness to be determined on the basis of a 20-minute EEG recording made with only four electrodes. This approach makes EEG assessments of awareness clinically viable, and therefore has potential for inclusion in a standard assessment of awareness in the VS

Geostatistical Models for the Prediction of Water Supply Network Failures in Bogotá, Integrating Machine Learning Algorithms

[EN] Currently new strategies of spatial referencing, data analysis, and machine learning methods are integrated with Geographical Information Systems (GISs) to understand specific characteristics and water supply dynamics. This work explores the variables that can cause spacial failures and potential risk areas with application to a zone in the Bogotá water supply network. Machine learning algorithms are proposed to generate prediction models and potential failure maps. A sensitivity analysis was held to identify the model with the best fit for the estimation. This study will allow water supply decisions makers to focalize their efforts in the field.[ES] Actualmente se buscan nuevas estrategias y/o metodologías basadas en la integración de los Sistemas de Información Geográfica (SIGs) como forma de georeferenciacion espacial y visualización de las variables analizadas, junto con métodos de aprendizaje automático (Machine Learning) que permitan entender características puntuales, variables influyentes y dinámicas de los sistemas de abastecimiento de agua potable.En este trabajo se hace la identificación espacial de los fallos y zonas potenciales de riesgo que se presentan en una zona de la red de abastecimiento de Bogotá, explorando las variables que puedan tener mayor incidencia en los mismos. Se propone el uso de algoritmos de aprendizaje automático para la generación de modelos de predicción y la elaboración de mapas de fallos potenciales, identificando, a través de un análisis de sensibilidad, cuál de estos modelos presenta un mejor ajuste en la estimación. Este estudio permite a los gestores del abastecimiento una localización precisa y eficiente de los fallos en la red, apoyando el proceso de toma de decisiones.Navarrete-López, CF.; Calderón-Rivera, D.; Díaz Arévalo, JL.; Herrera Fernández, AM.; Izquierdo Sebastián, J. (2018). Modelos geoestadísticos para la predicción de fallos de una zona de la red de abastecimiento de agua de Bogotá, integrando algoritmos de Machine Learning. Social Science Research Network. 1-8. https://doi.org/10.2139/ssrn.3113048S1

Massively Parallel Execution of Logic Programs: A Static approach

A static model for the parallel execution of logic programs on MIMD distributed memory systems is presented where a refutation is implemented through a process network returned by the compilation of the logic program. The model supports Restricted-AND, OR and stream parallelism and it is integrated with a set of static analyses to optimise the process network. Altogether, the processes interact according to a static data driven model with medium grain operators. Data flowing in the network is tagged to distinguish bindings belonging to the same refutation. A scheduling strategy to integrate low level scheduling and message flow control has been defined. Performance figures are presented

Heart Failure Artificial Intelligence-Based Computer Aided Diagnosis Telecare System

In this paper we present an Artificial Intelligence-based Computer Aided Diagnosis system designed to assist the clinical decision of non-specialist staff in the analysis of Heart Failure patients. The system computes the patient's pathological condition and highlights possible aggravations. The system is based on three functional parts: Diagnosis (severity assessing), Prognosis, and Follow-up management. Four Artificial Intelligence-based techniques are used and compared in diagnosis function: a Neural Network, a Support Vector Machine, a Decision Tree and a Fuzzy Expert System whose rules are produced by a Genetic Algorithm. In order to offer a complete HF analysis dashboard, state of the art algorithms are implemented to support a score-based prognosis function. The patient's Follow-up is used to refine the diagnosis by adding Heart Failure type information and to detect any worsening of patient's clinical status. In the Results section we compared the accuracy of the different implemented techniques. © 2012 Springer-Verlag