title: Neuro-computing versus linear statistical techniques applied to liver transplant monitoring: a comparative study.
abstract: This paper explores the potential for the application of neurocomputing in on-line monitoring in the liver transplantation domain. It extends our previously documented work to provide both an assessment of the performance gains achievable by incorporating temporal and dynamical information about the measurements made on a patient as well as presenting a novel computerized clinical decision aid for this domain. A comparison of the performance of linear and nonlinear classification system is made and used to motivate the final selection of the diagnostic inputs.
authors.affiliation: Department of Engineering, University of Cambridge, U.K. david.melvin@wax-info.com
authors.forename: D G
authors.initials: DG
authors.lastname: Melvin
authors.forename: M
authors.initials: M
authors.lastname: Niranjan
authors.forename: R W
authors.initials: RW
authors.lastname: Prager
authors.forename: A K
authors.initials: AK
authors.lastname: Trull
authors.forename: V F
authors.initials: VF
authors.lastname: Hughes
mesh_terms: D001698:Biomedical Engineering; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D006084:Graft Rejection / Q000209:etiology / Q000503:physiopathology; D006801:Humans; D016014:Linear Models; D008111:Liver Function Tests; D016031:Liver Transplantation / Q000009:adverse effects / Q000502:physiology*; D008954:Models, Biological; D016571:Neural Networks, Computer