Publication: Aplicación web y estudio de sistemas de visualización para un sistema de predicción de crisis en el trastorno de bipolaridad
Loading...
Official URL
Full text at PDC
Publication Date
2016
Authors
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Citations
Exportar
Abstract
Las tecnologías relacionadas con el análisis de datos masivos están empezando a revolucionar nuestra forma de vivir, nos demos cuenta de ello o no. Desde las grandes compañías, que utilizan big data para la mejora de sus resultados, hasta nuestros teléfonos, que lo usan para medir nuestra actividad física. La medicina no es ajena a esta tecnología, que puede utilizarla para mejorar los diagnósticos y establecer planes de seguimiento personalizados a los pacientes. En particular, el trastorno bipolar requiere de atención constante por parte de los profesionales médicos. Con el objetivo de contribuir a esta labor, se presenta una plataforma, denominada bip4cast, que pretende predecir con antelación las crisis de estos enfermos. Uno de sus componentes es una aplicación web creada para realizar el seguimiento a los pacientes y representar gráficamente los datos de que se dispone con el objetivo de que el médico sea capaz de evaluar el estado del paciente, analizando el riesgo de recaída.
Además, se estudian las diferentes visualizaciones implementadas en la aplicación con el objetivo de comprobar si se adaptan correctamente a los objetivos que se pretenden alcanzar con ellas. Para ello, generaremos datos aleatorios y representaremos estos gráficamente, examinando las posibles conclusiones que de ellos pudieran extraerse.
Massive data analysis technologies are revolutionating our way of life, whether we are aware or not. From big companies, which use big data to get better results to our smartphones, which use it to track our phisycal activities. Medicine can use big data too in order to improve patient’s diagnosis and make personalized treatments. In particular, bipolar disorder requieres continuous medical supervision. To contribute to that task we have developed an integrated platform, named bip4cast, which aims to predict patient’s crisis. This platform consists of a web pplication, an Android app and an activity monitor weared by the patient. The web app’s purpose is to track patient’s status and to make graphical representations using several charts of some of the data obtained from them. That way the doctor is able to evaluate the patient’s status and analysing what is the risk of having a crisis. In addition, some implemented charts are studied in order to check if they fit in their purpose to find the conection between the medicine, the patient’s status and the risk of having a crisis. To achieve this, we represent some some random generated data, studying the results which can be concluded from them.
Massive data analysis technologies are revolutionating our way of life, whether we are aware or not. From big companies, which use big data to get better results to our smartphones, which use it to track our phisycal activities. Medicine can use big data too in order to improve patient’s diagnosis and make personalized treatments. In particular, bipolar disorder requieres continuous medical supervision. To contribute to that task we have developed an integrated platform, named bip4cast, which aims to predict patient’s crisis. This platform consists of a web pplication, an Android app and an activity monitor weared by the patient. The web app’s purpose is to track patient’s status and to make graphical representations using several charts of some of the data obtained from them. That way the doctor is able to evaluate the patient’s status and analysing what is the risk of having a crisis. In addition, some implemented charts are studied in order to check if they fit in their purpose to find the conection between the medicine, the patient’s status and the risk of having a crisis. To achieve this, we represent some some random generated data, studying the results which can be concluded from them.
Description
Trabajo de Fin de Grado en Ingeniería Informática y Matemáticas (Universidad Complutense, Facultad de Informática, curso 2015/2016)