Title :
Deep learning architecture for data mining from surgical data
Author_Institution :
Tech. Univ. of Sofia, Sofia, Bulgaria
Abstract :
The paper addresses issues about data mining from surgical data. Surgical data has several distinguishing features - it is voluminous, heterogeneous, noise-prone, has low level of formalization due to lack of standardization in domain and highly correlated features. Architecture for data mining from surgical data, which deals with described features, is proposed. It is related to deep learning architectures as it consists of several hierarchical levels. Context-wise input layer modules process heterogeneous data. Results from these modules are calibrated before being passed to the next layer. At the last layer inference model for classification and prediction is derived.
Keywords :
data mining; inference mechanisms; learning (artificial intelligence); medical computing; medical information systems; pattern classification; surgery; classification; context-wise input layer module; data mining; deep learning architecture; heterogeneous data; highly correlated features; last layer inference model; noise-prone data; prediction; surgical data; voluminous data; Computer architecture; Data mining; Feature extraction; Medical diagnostic imaging; Noise; Surgery;
Conference_Titel :
MIPRO, 2012 Proceedings of the 35th International Convention
Conference_Location :
Opatija
Print_ISBN :
978-1-4673-2577-6
