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:1.8098
- @InProceedings{Borlikova:2017:GECCO,
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author = "Gilyana Borlikova and Michael O'Neill and
Louis Smith and Michael Phillips",
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title = "Development of a Multi-model System to Accommodate
Unknown Misclassification Costs in Prediction of
Patient Recruitment in Multicentre Clinical Trials",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference Companion",
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series = "GECCO '17",
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year = "2017",
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isbn13 = "978-1-4503-4939-0",
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address = "Berlin, Germany",
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pages = "263--264",
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size = "2 pages",
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URL = "http://doi.acm.org/10.1145/3067695.3076062",
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DOI = "doi:10.1145/3067695.3076062",
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acmid = "3076062",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "15-19 " # jul,
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keywords = "genetic algorithms, genetic programming, Grammatical
evolution",
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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.",
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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