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

Marina Theresia Strobl; Julia Vetter; Gerhard Halmerbauer; Tilman Königswieser; Stephan Winkler

doi:10.1007/978-3-031-25312-6_20

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

Marina Theresia Strobl, Julia Vetter, Gerhard Halmerbauer, Tilman Königswieser, Stephan Winkler

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

Abstract

We analyze data of 18,000 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.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2022 - 18th International Conference, Revised Selected Papers
Subtitle of host publication	18th International Conference, Las Palmas de Gran Canaria, Spain, February 20–25, 2022, Revised Selected Papers
Editors	Roberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia
Publisher	Springer
Pages	173-180
Number of pages	8
ISBN (Electronic)	978-3-031-25312-6
ISBN (Print)	978-3-031-25311-9
DOIs	https://doi.org/10.1007/978-3-031-25312-6_20
Publication status	Published - 10 Feb 2023
Event	18th International Conference on Computer Aided Systems Theory: EUROCAST 2022 - Las Palmas de Gran Canaria, Las Palmas, Spain Duration: 20 Feb 2022 → 25 Feb 2022 https://eurocast2022.fulp.ulpgc.es https://eurocast2022.fulp.ulpgc.es/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13789 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Computer Aided Systems Theory
Abbreviated title	Eurocast 2022
Country/Territory	Spain
City	Las Palmas
Period	20.02.2022 → 25.02.2022
Internet address	https://eurocast2022.fulp.ulpgc.es https://eurocast2022.fulp.ulpgc.es/

Access to Document

10.1007/978-3-031-25312-6_20

Cite this

Strobl, M. T., Vetter, J., Halmerbauer, G., Königswieser, T., & Winkler, S. (2023). Using Explainable Artificial Intelligence for Data Based Detection of Complications in Records of Patient Treatments. In R. Moreno-Díaz, F. Pichler, & A. Quesada-Arencibia (Eds.), Computer Aided Systems Theory – EUROCAST 2022 - 18th International Conference, Revised Selected Papers: 18th International Conference, Las Palmas de Gran Canaria, Spain, February 20–25, 2022, Revised Selected Papers (pp. 173-180). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13789 LNCS). Springer. https://doi.org/10.1007/978-3-031-25312-6_20

Strobl, Marina Theresia ; Vetter, Julia ; Halmerbauer, Gerhard et al. / Using Explainable Artificial Intelligence for Data Based Detection of Complications in Records of Patient Treatments. Computer Aided Systems Theory – EUROCAST 2022 - 18th International Conference, Revised Selected Papers: 18th International Conference, Las Palmas de Gran Canaria, Spain, February 20–25, 2022, Revised Selected Papers. editor / Roberto Moreno-Díaz ; Franz Pichler ; Alexis Quesada-Arencibia. Springer, 2023. pp. 173-180 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Using Explainable Artificial Intelligence for Data Based Detection of Complications in Records of Patient Treatments",

abstract = "We analyze data of 18,000 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.",

author = "Strobl, {Marina Theresia} and Julia Vetter and Gerhard Halmerbauer and Tilman K{\"o}nigswieser and Stephan Winkler",

note = "Funding Information: Acknowledgements. The work described in this paper was supported by LeiVMed, a joint research project of FH O{\"O} and O{\"O}G, as well as TIMED, FH O{\"O}{\textquoteright}s Center of Excellence for Technical Innovation in Medicine. The authors thank their colleague Louise Buur for careful proofreading of this paper. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 18th International Conference on Computer Aided Systems Theory : EUROCAST 2022 ; Conference date: 20-02-2022 Through 25-02-2022",

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booktitle = "Computer Aided Systems Theory – EUROCAST 2022 - 18th International Conference, Revised Selected Papers",

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url = "https://eurocast2022.fulp.ulpgc.es, https://eurocast2022.fulp.ulpgc.es/",

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Strobl, MT , Vetter, J , Halmerbauer, G, Königswieser, T & Winkler, S 2023, Using Explainable Artificial Intelligence for Data Based Detection of Complications in Records of Patient Treatments. in R Moreno-Díaz, F Pichler & A Quesada-Arencibia (eds), Computer Aided Systems Theory – EUROCAST 2022 - 18th International Conference, Revised Selected Papers: 18th International Conference, Las Palmas de Gran Canaria, Spain, February 20–25, 2022, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13789 LNCS, Springer, pp. 173-180, 18th International Conference on Computer Aided Systems Theory, Las Palmas, Spain, 20.02.2022. https://doi.org/10.1007/978-3-031-25312-6_20

Using Explainable Artificial Intelligence for Data Based Detection of Complications in Records of Patient Treatments. / Strobl, Marina Theresia ; Vetter, Julia ; Halmerbauer, Gerhard et al.
Computer Aided Systems Theory – EUROCAST 2022 - 18th International Conference, Revised Selected Papers: 18th International Conference, Las Palmas de Gran Canaria, Spain, February 20–25, 2022, Revised Selected Papers. ed. / Roberto Moreno-Díaz; Franz Pichler; Alexis Quesada-Arencibia. Springer, 2023. p. 173-180 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13789 LNCS).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

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AU - Königswieser, Tilman

AU - Winkler, Stephan

N1 - Funding Information: Acknowledgements. The work described in this paper was supported by LeiVMed, a joint research project of FH OÖ and OÖG, as well as TIMED, FH OÖ’s Center of Excellence for Technical Innovation in Medicine. The authors thank their colleague Louise Buur for careful proofreading of this paper. Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Strobl MT , Vetter J , Halmerbauer G, Königswieser T, Winkler S. Using Explainable Artificial Intelligence for Data Based Detection of Complications in Records of Patient Treatments. In Moreno-Díaz R, Pichler F, Quesada-Arencibia A, editors, Computer Aided Systems Theory – EUROCAST 2022 - 18th International Conference, Revised Selected Papers: 18th International Conference, Las Palmas de Gran Canaria, Spain, February 20–25, 2022, Revised Selected Papers. Springer. 2023. p. 173-180. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-25312-6_20

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

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