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Verfasst von:Kapsner, Lorenz A. [VerfasserIn]   i
 Feißt, Manuel [VerfasserIn]   i
 Purbojo, Ariawan [VerfasserIn]   i
 Prokosch, Hans-Ulrich [VerfasserIn]   i
 Ganslandt, Thomas [VerfasserIn]   i
 Dittrich, Sven [VerfasserIn]   i
 Mang, Jonathan M. [VerfasserIn]   i
 Wällisch, Wolfgang [VerfasserIn]   i
Titel:Using machine learning and feature importance to identify risk factors for mortality in pediatric heart surgery
Verf.angabe:Lorenz A. Kapsner, Manuel Feißt, Ariawan Purbojo, Hans-Ulrich Prokosch, Thomas Ganslandt, Sven Dittrich, Jonathan M. Mang and Wolfgang Wällisch
E-Jahr:2024
Jahr:18 November 2024
Umfang:23 S.
Illustrationen:Illustrationen
Fussnoten:Gesehen am 02.06.2025
Titel Quelle:Enthalten in: Diagnostics
Ort Quelle:Basel : MDPI, 2011
Jahr Quelle:2024
Band/Heft Quelle:14(2024), 22, Artikel-ID 2587, Seite 1-23
ISSN Quelle:2075-4418
Abstract:Background: The objective of this IRB-approved retrospective monocentric study was to identify risk factors for mortality after surgery for congenital heart defects (CHDs) in pediatric patients using machine learning (ML). CHD belongs to the most common congenital malformations, and remains the leading mortality cause from birth defects. Methods: The most recent available hospital encounter for each patient with an age <18 years hospitalized for CHD-related cardiac surgery between the years 2011 and 2020 was included in this study. The cohort consisted of 1302 eligible patients (mean age [SD]: 402.92 [±562.31] days), who were categorized into four disease groups. A random survival forest (RSF) and the ‘eXtreme Gradient Boosting’ algorithm (XGB) were applied to model mortality (incidence: 5.6% [n = 73 events]). All models were then applied to predict the outcome in an independent holdout test dataset (40% of the cohort). Results: RSF and XGB achieved average C-indices of 0.85 (±0.01) and 0.79 (±0.03), respectively. Feature importance was assessed with ‘SHapley Additive exPlanations’ (SHAP) and ‘Time-dependent explanations of machine learning survival models’ (SurvSHAP(t)), both of which revealed high importance of the maximum values of serum creatinine observed within 72 h post-surgery for both ML methods. Conclusions: ML methods, along with model explainability tools, can reveal interesting insights into mortality risk after surgery for CHD. The proposed analytical workflow can serve as a blueprint for translating the analysis into a federated setting that builds upon the infrastructure of the German Medical Informatics Initiative.
DOI:doi:10.3390/diagnostics14222587
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/10.3390/diagnostics14222587
 kostenfrei: Volltext: https://www.mdpi.com/2075-4418/14/22/2587
 DOI: https://doi.org/10.3390/diagnostics14222587
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:congenital heart defects (CHDs)
 eXtreme Gradient Boosting (XGB)
 feature importance
 machine learning (ML)
 mortality
 random survival forest (RSF)
 risk factors
K10plus-PPN:1927235871
Verknüpfungen:→ Zeitschrift

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