Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer’s disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis.
Data centers are power hungry facilities. Energy-aware task scheduling approaches are of utmost importance to improve energy savings in data centers, although they need to know beforehand the energy consumption of the applications that will run in the servers.
Primary progressive aphasia (PPA) is a neurodegenerative syndrome in which diagnosis is usually challenging. Biomarkers are needed for diagnosis and monitoring. In this study, we aimed to evaluate Electroencephalography (EEG) as a biomarker for the diagnosis of PPA.
This work presents a novel and promising approach to the clinical management of acute stroke. Using machine learning techniques, our research has succeeded in developing accurate diagnosis and prediction real-time models from hemodynamic data.
Chronic diseases benefit of the advances on personalize medicine coming out of the integrative convergence of significant developments in systems biology, the Internet of Things and Artificial Intelligence. 70% to 80% of all healthcare costs in the EU and US are currently spent on chronic diseases, leading to estimated costs of 700 billion and $3.5 trillion respectively.
Primary progressive aphasia (PPA) is a neurodegenerative syndrome for which no effective treatment is available. OBJECTIVE: We aimed to assess the effect of repetitive transcranial magnetic stimulation (rTMS), using personalized targeting.