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:1.7954
- @InProceedings{10.1007/978-3-319-55702-1_50,
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author = "Gilyana Borlikova and Michael Phillips and
Louis Smith and Miguel Nicolau and Michael O'Neill",
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editor = "Andreas Fink and Armin Fuegenschuh and
Martin Josef Geiger",
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title = "Alternative Fitness Functions in the Development of
Models for Prediction of Patient Recruitment in
Multicentre Clinical Trials",
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booktitle = "Operations Research Proceedings 2016",
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year = "2018",
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publisher = "Springer International Publishing",
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pages = "375--381",
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keywords = "genetic algorithms, genetic programming, Grammatical
evolution",
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URL = "https://link.springer.com/chapter/10.1007/978-3-319-55702-1_50",
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DOI = "doi:10.1007/978-3-319-55702-1_50",
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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.",
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isbn13 = "978-3-319-55702-1",
- }
Genetic Programming entries for
Gilyana Borlikova
Michael Phillips
Louis Smith
Miguel Nicolau
Michael O'Neill
Citations