On The Correlation Between Geo-Referenced Clinical Data And Remotely Sensed Air Pollution Maps.
This work presents an analysis framework enabling the integration of a clinical-administrative dataset of Type 2 Diabetes (T2D) patients with environmental information derived from air quality maps acquired from remote sensing data. The research has been performed within the EU project MOSAIC, which gathers T2D patients' data coming from Fondazione S. Maugeri (FSM) hospital and the Pavia local health care agency (ASL). The proposed analysis is aimed to highlight the complexity of the domain, showing the different perspectives that can be adopted when applying a data-driven approach to large variety of temporal, geo-localized data. We investigated a set of 899 patients, located in the Pavia area, and detected several patterns depicting how clinical facts and air pollution variations may be related.
Laboratory for Biomedical Informatics, University of Pavia, Italy.
Arianna
A
Dagliati
Telecommunications and Remote Sensing Laboratory, University of Pavia, Italy.
Andrea
A
Marinoni
Department of Purchasing and Control, ASL Pavia, Italy.
Carlo
C
Cerra
Telecommunications and Remote Sensing Laboratory, University of Pavia, Italy.
Paolo
P
Gamba
Laboratory for Biomedical Informatics, University of Pavia, Italy.
Riccardo
R
Bellazzi
D000397:Air Pollution / Q000032:analysis / Q000706:statistics & numerical data*; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology*; D040362:Geographic Information Systems / Q000706:statistics & numerical data*; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007558:Italy / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods*; D015995:Prevalence; D058998:Remote Sensing Technology / Q000706:statistics & numerical data*; D012307:Risk Factors; D062211:Spatio-Temporal Analysis; D013223:Statistics as Topic