2 research outputs found
Validation of Self-Quantification Xiaomi Band in a Clinical Sleep Unit
[Abstract]
Polysomnography (PSG) is currently the accepted gold standard for sleep studies, as it
measures multiple variables that lead to a clear diagnosis of any sleep disorder. However, it has
some clear drawbacks, since it can only be performed by qualified technicians, has a high cost and
complexity and is very invasive. In the last years, actigraphy has been used along PSG for sleep
studies. In this study, we intend to assess the capability of the new Xiaomi Mi Smart Band 5 to be
used as an actigraphy tool. Sleep measures from PSG and Xiaomi Mi Smart Band 5 recorded in the
same night will be obtained and further analysed to assess their concordance. For this analysis, we
perform a paired sample t-test to compare the different measures, Bland–Altman plots to evaluate the
level of agreement between the Mi Band and PSG and Epoch by Epoch analysis to study the ability of
the Mi Band to correctly identify PSG-defined sleep stages. This study belongs to the research field
known as participatory health, which aims to offer an innovative healthcare model driven by the
patients themselves, leading to civic empowerment and self-management of health
Electronic Health Records Exploitation Using Artificial Intelligence Techniques
[Abstract] The exploitation of electronic health records (EHRs) has multiple utilities, from predictive
tasks and clinical decision support to pattern recognition. Artificial Intelligence (AI) allows to extract
knowledge from EHR data in a practical way. In this study, we aim to construct a Machine Learning
model from EHR data to make predictions about patients. Specifically, we will focus our analysis on
patients suffering from respiratory problems. Then, we will try to predict whether those patients will
have a relapse in less than 6, 12 or 18 months. The main objective is to identify the characteristics that
seem to increase the relapse risk. At the same time, we propose an exploratory analysis in search
of hidden patterns among data. These patterns will help us to classify patients according to their
specific conditions for some clinical variables.Centro de Investigación de Galicia CITIC is funded by Consellería de Educación, Universidades e Formación Profesional from Xunta de Galicia and European Union (European Regional Development Fund—FEDER Galicia 2014-2020 Program) by grant ED431G 2019/01. Partially supported by the Spanish Ministry of Science (Challenges of Society 2019) PID2019-104323RB-C33Xunta de Galicia; ED431G 2019/0