Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Machine learning approaches for electronic health records phenotyping: a methodical review
110
Zitationen
5
Autoren
2022
Jahr
Abstract
Continued research in ML-based phenotyping is warranted, with emphasis on characterizing nuanced phenotypes, establishing reporting and evaluation standards, and developing methods to accommodate misclassified phenotypes due to algorithm errors in downstream applications.
Ähnliche Arbeiten
"Why Should I Trust You?"
2016 · 14.564 Zit.
A Comprehensive Survey on Graph Neural Networks
2020 · 8.840 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.407 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.882 Zit.
Artificial intelligence in healthcare: past, present and future
2017 · 4.484 Zit.