Using Explainable Artificial Intelligence for Data Based Detection of Complications in Records of Patient Treatments
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{DBLP:conf/eurocast/StroblVHKW22,
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author = "Marina Strobl and Julia Vetter and
Gerhard Halmerbauer and Tilman Koenigswieser and Stephan M. Winkler",
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title = "Using Explainable Artificial Intelligence for Data
Based Detection of Complications in Records of Patient
Treatments",
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booktitle = "18th International Conference on Computer Aided
Systems Theory, EUROCAST 2022",
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year = "2022",
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editor = "Roberto Moreno-Diaz and Franz Pichler and
Alexis Quesada-Arencibia",
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volume = "13789",
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series = "Lecture Notes in Computer Science",
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pages = "173--180",
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address = "Las Palmas de Gran Canaria, Spain",
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month = feb # " 20-25",
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publisher = "Springer",
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note = "Revised Selected Papers",
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keywords = "genetic algorithms, genetic programming, XAI",
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timestamp = "Mon, 05 Feb 2024 20:28:43 +0100",
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biburl = "https://dblp.org/rec/conf/eurocast/StroblVHKW22.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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URL = "https://doi.org/10.1007/978-3-031-25312-6_20",
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DOI = "doi:10.1007/978-3-031-25312-6_20",
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size = "8 pages",
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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