Validation of a Large Medical Database

Complex clinical problems involving huge experimental evidence require a preliminary validation of observed data. This may avoid biasing due to incorrect sampling and clarify the sample distribution by showing datainherent regularities. The paper describes the application of unsupervised models of neural networks to the analysis of a very large set of clinical records for the study of Osteoporosis. The main result obtained lies in showing the overall uniformity of the data distribution, which indicates a correct, unbiased sampling of the considered population.

• Type: Conference
• Year: 1995
• Where: CBMS
• Authors: Guido Rovetta, Patrizia Monteforte, Gerolamo Bianchi, Stefano Rovetta, Rodolfo Zunino
• Keywords: CBMS 1995, Complex Clinical Problems, Datainherent Regularities, Medical Imaging, Preliminary Validation