Using Explainable Artificial Intelligence for Data Based Detection of Complications in Records of Patient Treatments

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{DBLP:conf/eurocast/StroblVHKW22,

• author = "Marina Strobl and Julia Vetter and Gerhard Halmerbauer and Tilman Koenigswieser and Stephan M. Winkler",
• title = "Using Explainable Artificial Intelligence for Data Based Detection of Complications in Records of Patient Treatments",
• booktitle = "18th International Conference on Computer Aided Systems Theory, EUROCAST 2022",
• year = "2022",
• editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia",
• volume = "13789",
• series = "Lecture Notes in Computer Science",
• pages = "173--180",
• address = "Las Palmas de Gran Canaria, Spain",
• month = feb # " 20-25",
• publisher = "Springer",
• note = "Revised Selected Papers",
• keywords = "genetic algorithms, genetic programming, XAI",
• timestamp = "Mon, 26 Jun 2023 20:47:36 +0200",
• biburl = "https://dblp.org/rec/conf/eurocast/StroblVHKW22.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• URL = "https://doi.org/10.1007/978-3-031-25312-6_20",
• DOI = "doi:10.1007/978-3-031-25312-6_20",
• abstract = "We analyse data of 18000 patients for identifying models that are able to detect complications in the data of surgeries and other medical treatments. High quality detection models are found using data available for those patients, for whom general data as well as risk factors are available. For identifying these detection models we use explainable artificial intelligence, namely symbolic regression by genetic programming with three different levels of model complexity with respect to model size and complexity of functions used as building blocks for the identified models.",

}

Genetic Programming entries for Marina Strobl Julia Vetter Gerhard Halmerbauer Tilman Koenigswieser Stephan M Winkler

Citations