Alternative Fitness Functions in the Development of Models for Prediction of Patient Recruitment in Multicentre Clinical Trials

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Gilyana Borlikova and Michael Phillips and 
                 Louis Smith and Miguel Nicolau and Michael O'Neill",
  editor =       "Andreas Fink and Armin Fuegenschuh and 
                 Martin Josef Geiger",
  title =        "Alternative Fitness Functions in the Development of
                 Models for Prediction of Patient Recruitment in
                 Multicentre Clinical Trials",
  booktitle =    "Operations Research Proceedings 2016",
  year =         "2018",
  publisher =    "Springer International Publishing",
  pages =        "375--381",
  keywords =     "genetic algorithms, genetic programming, Grammatical
  URL =          "",
  DOI =          "doi:10.1007/978-3-319-55702-1_50",
  abstract =     "For a drug to be approved for human use, its safety
                 and efficacy need to be evidenced through clinical
                 trials. At present, patient recruitment is a major
                 bottleneck in conducting clinical trials. Pharma and
                 contract research organisations (CRO) are actively
                 looking into optimisation of different aspects of
                 patient recruitment. One of the avenues to approach
                 this business problem is to improve the quality of
                 selection of investigators/sites at the start of a
                 trial. This study builds upon previous work that used
                 Grammatical Evolution (GE) to evolve classification
                 models to predict the future patient enrolment
                 performance of investigators/sites considered for a
                 trial. Selection of investigators/sites, depending on
                 the business context, could benefit from the use of
                 either especially conservative or more liberal
                 predictive models. To address this business need,
                 decision-tree type classifiers were evolved using
                 different fitness functions to drive GE. The functions
                 compared were classical accuracy, balanced accuracy and
                 F-measure with different values of parameter beta. The
                 issue of models' generalisability was addressed by
                 introduction of a validation procedure. The predictive
                 power of the resultant GE-evolved models on the test
                 set was compared with performance of a range of machine
                 learning algorithms widely used for classification. The
                 results of the study demonstrate that flexibility of GE
                 induced classification models can be used to address
                 business needs in the area of patient recruitment in
                 clinical trials.",
  isbn13 =       "978-3-319-55702-1",

Genetic Programming entries for Gilyana Borlikova Michael Phillips Louis Smith Miguel Nicolau Michael O'Neill