Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Preoperative Prediction of Postoperative Infections Using Machine Learning and Electronic Health Record Data
15
Zitationen
7
Autoren
2023
Jahr
Abstract
Parsimonious preoperative models for predicting postoperative infection risk using EHR data were developed and showed comparable performance to existing American College of Surgeons National Surgical Quality Improvement Program risk models that use manual chart review. These models can be used to estimate risk-adjusted postoperative infection rates applied to large volumes of EHR data in a timely manner.
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.