Remote Assessment of Disease and Relapse– Central Nervous System-RADAR-CNS
Journal Title: Transactions on Machine Learning and Artificial Intelligence - Year 2017, Vol 5, Issue 4
Abstract
Remote Assessment of Disease and Relapse in Central Nervous System Disorders (RADARCNS) is a major international research project. It aims to develop new ways of measuring major depressive disorder, epilepsy and multiple sclerosis (MS) using wearable devices and smartphone technology. RADARCNS aims to improve people’s quality of life and change how depression, epilepsy and MS are managed and treated. Data from mobile devices can give a full picture of a person’s condition at a level of detail which was previously impossible. This offers the potential to detect changes in behavior, sleep, or mood before the individual themselves is aware of it. This could help them to predict or even avoid a relapse. To achieve this, we are creating a pipeline for developing, testing and implementing remote measurement technologies for depression, multiple sclerosis (MS) and epilepsy. Depression, multiple sclerosis and epilepsy are all disorders of the central nervous system. While the symptoms and disability experienced by individuals with each condition are different, they all have a significant effect on people’s wellbeing. For doctors and people with these longterm or chronic conditions, understanding how their disease changes over time can help with its management. But in chronic conditions most of the symptoms and episodes happen outside of the health care environment. Measuring individuals’ symptoms, mood and daily function continuously, could help people gain better insight into their condition. RADARCNS receives funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115902. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA.
Authors and Affiliations
J. M. Garzón-Rey, J. Aguilo
Keywords
Related Articles
- EP ID EP310068
- DOI 10.14738/tmlai.54.3293
- Views 62
- Downloads 0
SIMARECH 3: A New Application for the Governance of Scientific Research
In the international context of research assessment, many countries appear to move critically towards a quantitative evaluation policy, which would eventually complement or replace the traditional peer review system. How...
Use of the MOOC for a Change of Attitude Towards Science and its Teaching
This work describes conceptual changes in science and its teaching; Observed among 200 primary school teachers after the application of a didactic proposal (online training). We used a platform as training and learning s...
Application of Hybrid Machine Learning to Detect and Remove Malware
Anti-malware software traditionally employ methods of signature-based and heuristic-based detection. These detection systems need to be manually updated with new behaviors to detect new, unknown, or adapted malware. Our...
Handengineered Features vs Automated Features for Arabic Handwriting Recognition
This work aims to compare the learning features with Convolutional Neural Networks (CNNs) and the handcrafted features. In order to determine the relevant features among of those kinds. We consider our previous baseline...
Electrical Behavior of Solar Cell Based on ZnO/PS
Because of high loss of photovoltaic conversion due to reflection of incident photon by the silicon surface, we proposed in this work a single heterojunction solar cell model based on porous silicon (PS) and Zinc oxide t...