Sensitivity analysis and auto-calibration of ORYZA2000 using simulation-optimization framework
"GENES", a genetic algorithm shell developed by the authors, was used to optimize allocation of hospital resources for a small set of hypothetical patients. GENES creates a random population of rule sets of the IF..THEN type, which are variable in both the number of rules in each set and in the size of each rule. GENES applies each rule set to a patient data base, ranks the goodness of each set as applied, and uses the mechanisms of population genetics, i.e. mutation, crossover, inversion and survival of the fittest, to create a new, and often improved generation of rule sets. It also allows for the time dependent nature of medical tests, the possibility of injury associated with those tests, and the fact that results may not always be conclusive. Using 10-11 artificially created patients admitted under the diagnosis of possible gall bladder disease, a rule set was obtained which selected a testing strategy from a list of available hospital resources and correctly diagnosed all patients at minimum cost in no more than 3807 generations.