TY - JOUR
T1 - A Multiagent System for Dynamic Data Aggregation in Medical Research
AU - Dubovitskaya, Alevtina
AU - Urovi, Visara
AU - Barba, Imanol
AU - Aberer, Karl
AU - Schumacher, Michael Ignaz
N1 - data source: project is ISyPeM 2 (http://www.nano-tera.ch/projects/368.php), the founding body is Nano-Terra, a Swiss agency.
dataset from the University Hospital of Lausanne which we used to test the model and it’s in the context of the ISYPEM2 project.
PY - 2016
Y1 - 2016
N2 - The collection of medical data for research purposes is a challenging and long-lasting process. In an effort to accelerate and facilitate this process we propose a new framework for dynamic aggregation of medical data from distributed sources. We use agent-based coordination between medical and research institutions. Our system employs principles of peer-to-peer network organization and coordination models to search over already constructed distributed databases and to identify the potential contributors when a new database has to be built. Our framework takes into account both the requirements of a research study and current data availability. This leads to better definition of database characteristics such as schema, content, and privacy parameters. We show that this approach enables a more efficient way to collect data for medical research.
AB - The collection of medical data for research purposes is a challenging and long-lasting process. In an effort to accelerate and facilitate this process we propose a new framework for dynamic aggregation of medical data from distributed sources. We use agent-based coordination between medical and research institutions. Our system employs principles of peer-to-peer network organization and coordination models to search over already constructed distributed databases and to identify the potential contributors when a new database has to be built. Our framework takes into account both the requirements of a research study and current data availability. This leads to better definition of database characteristics such as schema, content, and privacy parameters. We show that this approach enables a more efficient way to collect data for medical research.
U2 - 10.1155/2016/9027457
DO - 10.1155/2016/9027457
M3 - Article
C2 - 27975063
SN - 2314-6133
VL - 2016
JO - BioMed Research International
JF - BioMed Research International
M1 - 9027457
ER -