Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine
76
Zitationen
8
Autoren
2021
Jahr
Abstract
There was limited research in preserving the performance of machine learning models in the presence of temporal dataset shift in clinical medicine. Future research could focus on the impact of dataset shift on clinical decision making, benchmark the mitigation strategies on a wider range of datasets and tasks, and identify optimal strategies for specific settings.
Ähnliche Arbeiten
"Why Should I Trust You?"
2016 · 14.518 Zit.
A Comprehensive Survey on Graph Neural Networks
2020 · 8.809 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.371 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.827 Zit.
Artificial intelligence in healthcare: past, present and future
2017 · 4.469 Zit.