Named entity recognition of follow-up and time information in 20,000 radiology reports

Yan Xu, Junichi Tsujii, and Eric Chang

Abstract

Objective: To develop a system to extract follow-up

information from radiology reports. The method may be

used as a component in a system which automatically

generates follow-up information in a timely fashion.

Methods: A novel method of combining an LSP (labeled

sequential pattern) classifier with a CRF (conditional

random field) recognizer was devised. The LSP classifier

filters out irrelevant sentences, while the CRF recognizer

extracts follow-up and time phrases from candidate

sentences presented by the LSP classifier.

Measurements: The standard performance metrics of

precision (P), recall (R), and F measure (F) in the exact

and inexact matching settings were used for evaluation.

Results: Four experiments conducted using 20 000

radiology reports showed that the CRF recognizer

achieved high performance without time-consuming

feature engineering and that the LSP classifier further

improved the performance of the CRF recognizer. The

performance of the current system is P¼0.90, R¼0.86,

F¼0.88 in the exact matching setting and P¼0.98,

R¼0.93, F¼0.95 in the inexact matching setting.

Conclusion: The experiments demonstrate that the

system performs far better than a baseline rule-based

system and is worth considering for deployment trials in

an alert generation system. The LSP classifier

successfully compensated for the inherent weakness of

CRF, that is, its inability to use global information.

Details

Publication typeArticle
Published inJournal of the American Medical Informatics Association
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