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Verfasst von:Sirazitdinov, Andrei [VerfasserIn]   i
 Buchwald, Marcus [VerfasserIn]   i
 Heuveline, Vincent [VerfasserIn]   i
 Hesser, Jürgen [VerfasserIn]   i
Titel:Graph neural networks for individual treatment effect estimation
Titelzusatz:methods
Verf.angabe:Andrei Sirazitdinov, Marcus Buchwald, Vincent Heuveline, and Jürgen Hesser, (Member, IEEE)
E-Jahr:2024
Jahr:02 August 2024
Umfang:11 S.
Fussnoten:Gesehen am 22.10.2024
Titel Quelle:Enthalten in: Institute of Electrical and Electronics EngineersIEEE access
Ort Quelle:New York, NY : IEEE, 2013
Jahr Quelle:2024
Band/Heft Quelle:12(2024), Seite 106884-106894
ISSN Quelle:2169-3536
Abstract:Individual treatment effect (ITE) estimation is an important task for personalized decision-making in clinical settings. However, the data used to train an ITE estimation model may be limited. In this case, we expect that information regarding causal connectivity within features can facilitate model training and thus lead to better predictions. In this study, we incorporated causal information about the connectivity within features represented by a Directed Acyclic Graph (DAG) into the problem of ITE estimation. For this purpose, we propose a novel method based on Graph Neural Networks (GNN). Our results show that the proposed approach performs comparably to the current state-of-the-art methods on existing datasets. Using an artificial dataset, we demonstrate the potential advantages of using real graphs responsible for the data generation process over empty graphs with no edges. These advantages are particularly evident for datasets with limited training sizes and correctly defined DAGs. These findings highlight the potential of GNNs in personalized medicine for improving the assessment of individual treatment effects.
DOI:doi:10.1109/ACCESS.2024.3437665
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.1109/ACCESS.2024.3437665
 kostenfrei: Volltext: http://ieeexplore.ieee.org/document/10621010
 DOI: https://doi.org/10.1109/ACCESS.2024.3437665
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Causal inference
 Computational modeling
 Estimation
 graph neural networks
 Graph neural networks
 individual treatment effect estimation
 Reviews
 Task analysis
 Training
 Vectors
K10plus-PPN:1906378681
Verknüpfungen:→ Zeitschrift

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