Synthesizing Effective Diagnostic Models from Small Samples Using Structural Machine Learning: A Case Study in Automating COVID-19 Diagnosis

Created by W.Langdon from gp-bibliography.bib Revision:1.7650

@InProceedings{kaszuba:2023:GECCOcomp,

• author = "Piotr Kaszuba and Andrew Turner and Bartosz Mikulski and Nl Shasha Jumbe and Andreas Schuh and Michael Morimoto and Peter Rexelius and Ryan Hafen and Ron Deiotte and Kevin Hammond and Jerry Swan and Krzysztof Krawiec",
• title = "Synthesizing Effective Diagnostic Models from Small Samples Using Structural Machine Learning: A Case Study in Automating {COVID-19} Diagnosis",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
• pages = "727--730",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, machine learning, COVID-19, structural machine learning, domain-specific languages: Poster",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3590598",
• size = "4 pages",
• abstract = "The global COVID-19 pandemic has demonstrated the urgent need for diagnostic tools that can be both readily applied and dynamically calibrated by non-specialists, in terms of a sensitivity/specificity tradeoff that complies with relevant healthcare policies and procedures. This article describes the design and deployment of a novel machine learning algorithm, Structural Machine Learning (SML), that combines memetic grammar-guided program synthesis with self-supervised learning in order to learn effectively from small data sets while remaining relatively resistant to overfitting. SML is used to construct a signal processing pipeline for audio time-series, which then serves as the diagnostic mechanism for a wide-spectrum, infrasound-to-ultrasound e-stethoscope. In blind trials supervised by a third party, SML is shown to be superior to Deep Learning approaches in terms of the area under the ROC curve, while allowing for transparent interpretation of the decision-making process.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Piotr Kaszuba Andrew James Turner Bartosz Mikulski Nl Shasha Jumbe Andreas Schuh Michael Morimoto Peter Rexelius Ryan Hafen Ron Deiotte Kevin Hammond Jerry Swan Krzysztof Krawiec

Citations