• Using Python and SAS to Effici...

Treffer: Using Python and SAS to Efficiently Process Cancer Incidence Data in NAACCR XML Format.

Title:
Using Python and SAS to Efficiently Process Cancer Incidence Data in NAACCR XML Format.
Authors:
Liu T, Dong X, Ren Y, Zhang K, Galin O, Wilson R
Source:
Journal of registry management [J Registry Manag] Fall 2020; Vol. 47 (3), pp. 170-171.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: National Cancer Registrars Association, Inc Country of Publication: United States NLM ID: 9804163 Publication Model: Print Cited Medium: Print ISSN: 1945-6123 (Print) Linking ISSN: 19456131 NLM ISO Abbreviation: J Registry Manag Subsets: MEDLINE
Imprint Name(s):
Publication: Alexandria, VA : National Cancer Registrars Association, Inc.
Original Publication: Lenexa, KS : National Cancer Registrars Association,
MeSH Terms:
Neoplasms*/epidemiology , Software*, Databases, Factual ; Humans ; Incidence ; Registries
Entry Date(s):
Date Created: 20210615 Date Completed: 20210623 Latest Revision: 20210623
Update Code:
20250114
PMID:
34128923
Database:
MEDLINE