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

Publikation: Beitrag in Buch/Bericht/Tagungsband › Konferenzbeitrag › Begutachtung

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.

Originalsprache	Englisch
Titel	Computer Aided Systems Theory – EUROCAST 2022 - 18th International Conference, Revised Selected Papers
Untertitel	18th International Conference, Las Palmas de Gran Canaria, Spain, February 20–25, 2022, Revised Selected Papers
Redakteure/-innen	Roberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia
Herausgeber (Verlag)	Springer
Seiten	173-180
Seitenumfang	8
ISBN (elektronisch)	978-3-031-25312-6
ISBN (Print)	978-3-031-25311-9
DOIs	https://doi.org/10.1007/978-3-031-25312-6_20
Publikationsstatus	Veröffentlicht - 10 Feb. 2023
Veranstaltung	18th International Conference on Computer Aided Systems Theory Eurocast 2022: EUROCAST 2022 - Las Palmas de Gran Canaria, Las Palmas, Spanien Dauer: 20 Feb. 2022 → 25 Feb. 2022 https://eurocast2022.fulp.ulpgc.es https://eurocast2022.fulp.ulpgc.es/

Publikationsreihe

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

Konferenz

Konferenz	18th International Conference on Computer Aided Systems Theory Eurocast 2022
Kurztitel	Eurocast 2022
Land/Gebiet	Spanien
Ort	Las Palmas
Zeitraum	20.02.2022 → 25.02.2022
Internetadresse	https://eurocast2022.fulp.ulpgc.es https://eurocast2022.fulp.ulpgc.es/

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

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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 (Hrsg.), 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 (S. 173-180). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 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. Hrsg. / Roberto Moreno-Díaz ; Franz Pichler ; Alexis Quesada-Arencibia. Springer, 2023. S. 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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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 (Hrsg.), 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), Bd. 13789 LNCS, Springer, S. 173-180, 18th International Conference on Computer Aided Systems Theory
Eurocast 2022, Las Palmas, Spanien, 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. Hrsg. / Roberto Moreno-Díaz; Franz Pichler; Alexis Quesada-Arencibia. Springer, 2023. S. 173-180 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13789 LNCS).

Publikation: Beitrag in Buch/Bericht/Tagungsband › Konferenzbeitrag › Begutachtung

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T1 - Using Explainable Artificial Intelligence for Data Based Detection of Complications in Records of Patient Treatments

AU - Strobl, Marina Theresia

AU - Vetter, Julia

AU - Halmerbauer, Gerhard

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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Y1 - 2023/2/10

N2 - 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.

AB - 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.

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

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SN - 978-3-031-25311-9

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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

A2 - Moreno-Díaz, Roberto

A2 - Pichler, Franz

A2 - Quesada-Arencibia, Alexis

PB - Springer

T2 - 18th International Conference on Computer Aided Systems Theory

Y2 - 20 February 2022 through 25 February 2022

ER -

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, Hrsg., 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. S. 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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