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Automated information extraction from free‐text medical documents for stroke key performance indicators: a pilot study.

Authors :
Bacchi, Stephen
Gluck, Sam
Koblar, Simon
Jannes, Jim
Kleinig, Timothy
Source :
Internal Medicine Journal. Feb2022, Vol. 52 Issue 2, p315-317. 3p.
Publication Year :
2022

Abstract

Automated information extraction might be able to assist with the collection of stroke key performance indicators (KPI). The feasibility of using natural language processing for classification‐based KPI and datetime field extraction was assessed. Using free‐text discharge summaries, random forest models achieved high levels of performance in classification tasks (area under the receiver operator curve 0.95–1.00). The datetime field extraction method was successful in 29 of 43 (67.4%) cases. Further studies are indicated. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14440903
Volume :
52
Issue :
2
Database :
Academic Search Index
Journal :
Internal Medicine Journal
Publication Type :
Academic Journal
Accession number :
155361266
Full Text :
https://doi.org/10.1111/imj.15678