Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Interpretable/Explainable Predictive Modeling with Perioperative Dataset
0
Zitationen
3
Autoren
2025
Jahr
Abstract
This study focuses on analyzing a perioperative dataset to extract knowledge and gain valuable insights for developing predictive models that enable real-time risk assessment and early intervention strategies for patients. We performed our analysis on several fronts, including K-means Clustering, Association Rule Mining, and SHAP analysis. Moreover, we developed two deep Learning models based on MambaNet and TabNet, achieving high AUROC scores of 0.946 and 0.923, respectively. The model’s objective is to predict 30-day mortality after surgery.
Ähnliche Arbeiten
"Why Should I Trust You?"
2016 · 14.333 Zit.
A Comprehensive Survey on Graph Neural Networks
2020 · 8.696 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.221 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.640 Zit.
Artificial intelligence in healthcare: past, present and future
2017 · 4.414 Zit.