Evolving Classification Models for Prediction of Patient Recruitment in Multicentre Clinical Trials Using Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/evoW/BorlikovaPSO16,
  author =       "Gilyana Borlikova and Michael Phillips and 
                 Louis Smith and Michael O'Neill",
  title =        "Evolving Classification Models for Prediction of
                 Patient Recruitment in Multicentre Clinical Trials
                 Using Grammatical Evolution",
  booktitle =    "19th European Conference on Applications of
                 Evolutionary Computation, EvoApplications 2016",
  year =         "2016",
  editor =       "Giovanni Squillero and Paolo Burelli",
  volume =       "9597",
  series =       "Lecture Notes in Computer Science",
  pages =        "46--57",
  address =      "Porto, Portugal",
  month =        mar # " 30 -- " # apr # " 1",
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution, Clinical trials, Enrolment, Grammar-based
                 genetic programming",
  bibdate =      "2016-03-23",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/evoW/evoappl2016-1.html#BorlikovaPSO16",
  isbn13 =       "978-3-319-31204-0",
  DOI =          "doi:10.1007/978-3-319-31204-0_4",
  abstract =     "Successful and timely completion of prospective
                 clinical trials depends on patient recruitment as
                 patients are critical to delivery of the prospective
                 trial data. There exists a pressing need to develop
                 better tools/techniques to optimise patient recruitment
                 in multi-centre clinical trials. In this study
                 Grammatical Evolution (GE) is used to evolve
                 classification models to predict future patient
                 enrolment performance of investigators/site to be
                 selected for the conduct of the trial. Prediction
                 accuracy of the evolved models is compared with results
                 of a range of machine learning algorithms widely used
                 for classification. The results suggest that GE is able
                 to successfully induce classification models and
                 analysis of these models can help in our understanding
                 of the factors providing advanced indication of a trial
                 sites' future performance.",
  notes =        "EvoApplications2016 held inconjunction with
                 EuroGP'2016, EvoCOP2016 and EvoMUSART 2016",
}

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

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