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:1.7630

@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