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Automated extraction and normalization of findings from cancer-related free-text radiology reports.

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Mamlin BW, Heinze DT, McDonald CJ
AMIA Annu Symp Proc. 2003:420-4.
Abstract: 

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.

Mamlin BW, Heinze DT, McDonald CJ. Automated extraction and normalization of findings from cancer-related free-text radiology reports. AMIA Annu Symp Proc. 2003:420-4.