Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Ensemble machine learning methods in screening electronic health records: A scoping review
15
Zitationen
7
Autoren
2023
Jahr
Abstract
Our work highlights the importance of deriving and comparing the performances of different types of ensemble machine learning models when screening electronic health records and underscores the need for more comprehensive reporting of machine learning methodologies employed in clinical research.
Ähnliche Arbeiten
"Why Should I Trust You?"
2016 · 14.286 Zit.
A Comprehensive Survey on Graph Neural Networks
2020 · 8.651 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.177 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.575 Zit.
Artificial intelligence in healthcare: past, present and future
2017 · 4.404 Zit.