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Improving prevalence estimation through data fusion: methods and validation

Author
Aluja, T.; Daunis, J.; Brunsó, N.; Mompart, A.
Type of activity
Journal article
Journal
BMC medical informatics and decision making
Date of publication
2015-06-24
Volume
2015
First page
15
Last page
49
DOI
https://doi.org/10.1186/s12911-015-0169-z Open in new window
Repository
http://hdl.handle.net/2117/80031 Open in new window
URL
http://www.biomedcentral.com/1472-6947/15/49 Open in new window
Abstract
Estimation of health prevalences is usually performed with a single survey. Some attempts have been made to integrate more than one source of data. We propose here to validate this approach through data fusion. Data Fusion is the process of integrating two sources of data into one combined file. It allows us to take even greater advantage of existing information collected in databases. Here, we use data fusion to improve the estimation of health prevalences for two primary health factors: cardio...
Citation
Aluja, T., Daunis, J., Brunsó, N., Mompart, A. Improving prevalence estimation through data fusion: methods and validation. "BMC medical informatics and decision making", 24 Juny 2015, vol. 2015, p. 15-49.
Keywords
Cardio vascular diseases, Diabetes, Multiple imputation, Population surveys, Prevalences, Sequential regression
Group of research
IMP - Information Modelling and Processing
inLab FIB

Participants

  • Aluja Banet, Tomas  (author)
  • Daunis Estadella, Josep  (author)
  • Brunsó, Núria  (author)
  • Mompart Penina, Anna  (author)

Attachments