Decision Support Systems for Tele-medicine Applications
Economou (network operations, The Hellenic Open U.) proposes a new decision support system to use when dealing with data from a variety of sources and disciplines. He uses a combination of artificial neural networks (ANNs) in a flexible structure to process data with a minimum of pre-processing, an architecture eminently suited to patient-centered
What people are saying - Write a review
We haven't found any reviews in the usual places.
List of Figures
Implementation of the M DSS in Hardware
7 other sections not shown
accuracy adaptation ANN's ANNs application field architecture artificial intelligence Artificial Neural Networks CDDM Charokopos classification constitute convergence correlation decision support system defined demands diagnosis digits DSSs Economopoulos elements ensures experience Expert Systems exploited FFA-ANNs final findings FPGA Goutis haematology hardware description language Hidden Layer hidden Neurons hidden Slabs his/her IEEE IEEE Trans implementation in hardware induction inference engine input data input vector integrated interface intermediate decisions IUnknown knowledge base laboratory examinations large number learning algorithm Learning Patterns Level logical Lymberopoulos mapping MDSS MDSSs medical data medical decision support medicine methodology Microprogramming module MS-SQL novel Medical System operation output vector parameters patients Proc procedure processing proposed pseudo-input pulmonary diseases Pulmonology response server Sigmoid Sigmoid function specialized MDs specific structure symptoms Synapses TMS application training algorithms training Patterns tumour TWPL component utilization values VHDL Weights