Development of a Multi-model System to Accommodate Unknown Misclassification Costs in Prediction of Patient Recruitment in Multicentre Clinical Trials

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

@InProceedings{Borlikova:2017:GECCO,
  author =       "Gilyana Borlikova and Michael O'Neill and 
                 Louis Smith and Michael Phillips",
  title =        "Development of a Multi-model System to Accommodate
                 Unknown Misclassification Costs in Prediction of
                 Patient Recruitment in Multicentre Clinical Trials",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "263--264",
  size =         "2 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3076062",
  DOI =          "doi:10.1145/3067695.3076062",
  acmid =        "3076062",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution",
  abstract =     "Clinical trials are an essential step in a new drug's
                 approval process. Optimisation of patient recruitment
                 is one of the major challenges facing pharma and
                 contract research organisations (CRO) in conducting
                 multicentre clinical trials. Improving the quality of
                 selection of investigators/sites at the start of a
                 trial can help to address this business problem.
                 Grammatical Evolution (GE) was previously used to
                 evolve classification models to predict the future
                 patient enrolment performance of investigators/sites
                 considered for a trial. However, the unknown target
                 misclassification costs at the model development stage
                 pose additional challenges. To address them we use a
                 new composite fitness function to develop a multi-model
                 system of decision-tree type classifiers that optimise
                 a range of possible trade-offs between the correct
                 classification and errors. The predictive power of the
                 GE-evolved models is compared with a range of machine
                 learning algorithms widely used for classification. The
                 results of the study demonstrate that the GE-evolved
                 multi-model system can help to circumvent uncertainty
                 at the model development stage by providing a
                 collection of customised models for rapid deployment in
                 response to business needs of a clinical trial.",
  notes =        "Also known as
                 \cite{Borlikova:2017:DMS:3067695.3076062} GECCO-2017 A
                 Recombination of the 26th International Conference on
                 Genetic Algorithms (ICGA-2017) and the 22nd Annual
                 Genetic Programming Conference (GP-2017)",
}

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

Citations