title: DIMSUM: an expert system for multiexponential model discrimination.
abstract: DIMSUM is a highly automated, rule-based expert system designed to fit multiexponential models of increasing dimension to time series data, followed by selection of the best candidate model based on a user-modifiable and weighted decision tree of statistical criteria for model discrimination. The major features of DIMSUM are 1) an interactive and friendly user interface; 2) options for incorporating prior information about the parameters, the data, and/or the system from which the data were collected, in the form of equality and inequality constraints; 3) a built-in algorithm for automatically obtaining starting values for parameter estimation; 4) a robust weighted least-squares parameter estimation algorithm operating in an adaptive, user-adjustable search space; 5) comprehensive statistical results comparing different order candidate models fitted to the data; and 6) a novel, user-modifiable (learning) rule-based advisory subsystem providing an "expert's" interpretation of these statistical results and an explanation of all advice.
authors.affiliation: Biocybernetics Laboratory, University of California, Los Angeles 90024-1596.
authors.forename: A T
authors.initials: AT
authors.lastname: Marino
authors.forename: J J
authors.initials: JJ
authors.lastname: Distefano
authors.forename: E M
authors.initials: EM
authors.lastname: Landaw
mesh_terms: D000465:Algorithms; D000818:Animals; D016002:Discriminant Analysis; D005103:Expert Systems; D006801:Humans; D008954:Models, Biological; D010827:Physiology / Q000379:methods*