Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Evaluation of Automated Public De-Identification Tools on a Corpus of Radiology Reports
22
Zitationen
5
Autoren
2020
Jahr
Abstract
PHI appeared infrequently within the corpus of reports studied, which created difficulties for training machine learning systems. Out-of-the-box de-identification tools achieved limited performance on the corpus of radiology reports, suggesting the need for further advancements in public datasets and trained models.<i>Supplemental material is available for this article.</i>See also the commentary by Tenenholtz and Wood in this issue.© RSNA, 2020.
Ähnliche Arbeiten
Refinement and reassessment of the SERVQUAL scale.
1991 · 3.966 Zit.
Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review
2005 · 3.771 Zit.
Radiobiology for the Radiologist.
1974 · 3.501 Zit.
International evidence-based recommendations for point-of-care lung ultrasound
2012 · 2.814 Zit.
Radiation Dose Associated With Common Computed Tomography Examinations and the Associated Lifetime Attributable Risk of Cancer
2009 · 2.431 Zit.