Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Automated extraction and normalization of findings from cancer-related free-text radiology reports.
59
Zitationen
3
Autoren
2003
Jahr
Abstract
UNLABELLED: We describe the performance of a particular natural language processing system that uses knowledge vectors to extract findings from radiology reports. LifeCode (A-Life Medical, Inc.) has been successfully coding reports for billing purposes for several years. In this study, we describe the use of LifeCode to code all findings within a set of 500 cancer-related radiology reports against a test set in which all findings were manually tagged. The system was trained with 1400 reports prior to running the test set. RESULTS: LifeCode had a recall of 84.5% and precision of 95.7% in the coding of cancer-related radiology report findings. CONCLUSION: Despite the use of a modest sized training set and minimal training iterations, when applied to cancer-related reports the system achieved recall and precision measures comparable to other reputable natural language processors in this domain.
Ähnliche Arbeiten
Refinement and reassessment of the SERVQUAL scale.
1991 · 3.967 Zit.
Radiobiology for the Radiologist.
1974 · 3.502 Zit.
ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee
2017 · 2.422 Zit.
Accuracy of Physician Self-assessment Compared With Observed Measures of Competence
2006 · 2.324 Zit.
Technology as an Occasion for Structuring: Evidence from Observations of CT Scanners and the Social Order of Radiology Departments
1986 · 2.247 Zit.