Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count results
12
Zitationen
2
Autoren
2022
Jahr
Abstract
This study provides preliminary evidence supporting the value of machine learning for detecting WBIT errors affecting CBC results. Although further work addressing practical issues is required, substantial patient-safety benefits await the successful deployment of machine learning models for WBIT error detection.
Ähnliche Arbeiten
STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
1986 · 47.116 Zit.
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
1982 · 21.449 Zit.
pROC: an open-source package for R and S+ to analyze and compare ROC curves
2011 · 13.538 Zit.
Basic principles of ROC analysis
1978 · 6.028 Zit.
All About Albumin: Biochemistry, Genetics, and Medical Applications
1995 · 3.103 Zit.