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Erschienen in:
Enhanced Multiobjective Population-Based Incremental Learning with Applications in Risk Treaty Optimization Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

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

verfasst von : Gilyana Borlikova, Michael Phillips, Louis Smith, Michael O’Neill

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer International Publishing

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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 multicentre 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.

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Vorheriges Kapitel Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Data
Nächstes Kapitel Portfolio Optimization, a Decision-Support Methodology for Small Budgets
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Metadaten
Titel
Evolving Classification Models for Prediction of Patient Recruitment in Multicentre Clinical Trials Using Grammatical Evolution
verfasst von
Gilyana Borlikova
Michael Phillips
Louis Smith
Michael O’Neill
Copyright-Jahr
2016
Buch
Applications of Evolutionary Computation

Print ISBN: 978-3-319-31203-3

Electronic ISBN: 978-3-319-31204-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-31204-0_4

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